kristen brent venable

Kristen Brent Venable

ContactInformation Florida Institute of Human and Machine Cognition

40 S Alcaniz St E-mail: [email protected], FL 32502 Web:www.ihmc.us/groups/bvenable

Department of Intelligent Systems and RoboticsUniversity of West Florida100 S. Alcaniz St.Building 545, Suite B Cell: +1-214-364-0739

E-mail: [email protected], FL 32502 Web:https://uwf.edu/intelligent-

systems-and-robotics/our-faculty/uwf-faculty/dr-kristin-brent-venable.html

AcademicEmployment Current:

Senior Research Scientist, Florida Institute of Human and Machine Cognition (IHMC), Pen-sacola, FL, USA. August 2019 – present.

Director, Intelligent Systems and Robotics Program, University of West Florida, Pensacola,FL, USA. August 2019 – present.

Professor of Computer Science, Department of Computer Science, University of West Florida,Pensacola, FL, USA. August 2019 – present.

Research Professor of Computer Science, Department of Computer Science, Tulane Uni-versity, New Orleans, LA, USA. August 2019 – present.

Past:

Professor of Computer Science, Department of Computer Science, Tulane University, NewOrleans, LA, USA. July 2018 – June 2019.

Associate Professor of Computer Science, Department of Computer Science, Tulane Uni-versity, New Orleans, LA, USA. July 2012 – July 2018.

Research Scientist, Florida Institute of Human and Machine Cognition (IHMC), Ocala, FL,USA. August 2012 – July 2019.

Tenured Assistant Professor of Computer Science, Department of Pure and AppliedMathematics, University of Padova, Italy. March 2009–July 2012.

Non-Tenured Assistant Professor of Computer Science, Department of Pure and AppliedMathematics, University of Padova, Italy. March 2006–February 2009.

1 of 24

http://www.ihmc.us

mailto:[email protected]

http://www.tedpavlic.com/

https://uwf.edu/intelligent-systems-and-robotics/

https://uwf.edu/intelligent-systems-and-robotics/

mailto:[email protected]





Post Doc in Computer Science, Department of Pure and Applied Mathematics, Universityof Padova, Italy. June 2005–February 2006.

Education

P.h.D., Computer Science, University of Padova, Italy. May 2005.Thesis Title: Reasoning with Preferences over Temporal, Uncertain, and Conditional Statements.Supervisor: Francesca Rossi. Reviewers: Fahiem Bacchus, Thomas Schiex. Evaluation: Excel-lent.

Laurea Magna cum Laude, Mathematics, University of Padova, Italy. October 2001.Thesis Title: Solving and Learning Soft Temporal Constraints. Supervisors: Francesca Rossi,Alessandro Sperduti. Co-supervisors: Lina Khatib, Robert Morris, Paul Morris. Equivalent toA.B. plus M.S..

ResearchAreas Artificial intelligence; preferences; constraints; multi-agent systems; temporal reasoning; uncer-

tainty; soft constraints; compact preference models; computational social choice; social choiceand voting; preference elicitation; preference aggregation; resource-bounded reasoning; computa-tional and communication complexity; optimization; search; scheduling; controllability; cognitivemodelling; machine learning.

ResearchInterests My research has been dedicated to providing a solid framework for the design and deployment

of intelligent systems able to reason about preferences. As preferences are fundamental for theanalysis of human choice behavior, they are becoming of increasing importance for computationalfields such as artificial intelligence (AI), databases, and human-computer interaction. Preferencemodels are needed in decision-support systems such as web-based recommender systems, in au-tomated problem solvers such as configurators, and in autonomous systems such as Mars rovers.Moreover, social choice methods are also of key importance in computational domains such asmulti-agent systems.I have investigated preferences from a single agent as well as from a multi-agent perspective.I have been active in the pioneering line of research involving the representation of an agent’s pref-erences via soft constraints. I have substantially contributed in making such a framework one ofthe most popular AI-based frameworks for reasoning about preferences. Exploiting the flexibilityallowed by the underlying model, I have designed and deployed several extensions encompassingbipolar preferences and different kinds of uncertainty. In pursuit of a single unifying AI frameworkfor preferences, I have investigated the relationship among different AI-based preference modelsand I have designed hybrids allowing to exploit the key features of each model. From a multi-agent point of view, I have devoted significant effort to the study of settings where multiple agentshave to reach a common decision based on their individual preferences. In this respect, I havecontributed to the establishment of a new research area, computational social choice, bridging thegap between social choice (and specifically voting theory) and computer science with respect tomethods to aggregate preferences.A parallel line of research I have been pursuing since the start of my career regards constraint-based temporal reasoning. In this respect I have contributed to the extension of the main reasoningframeworks to incorporate both preferences and uncertainty, allowing one to find solutions thatare, at the same time, optimal with respect to preferences and robust with respect to uncertainty.I have been recently involved in several interdisciplinary and application projects. I am collab-orating with researchers from NASA Ames on efficient scheduling if data-transfers for missionsinvolving small satellites. In the context of cognitive modeling and neuroscience, I am leadingthe development of a constraint-based model of how the human brain allocates attention of theauditory system. I am also actively involved in an interdisciplinary theme which has currentlybeen under the spotlight: AI for social good. In this respect, I am collaborating with sociolo-gists, psychologists and environmental scientists in developing content personalization to foster

2 of 24

resilience of Gulf communities to environmental stressors such as oil spills and hurricanes. I amalso investigating how constraints and preference frameworks can be a mean to embed ethicalrequirements into artificial agents.

Publications

Dissertations

2. P.h.D., Computer Science,. K. Brent Venable. Reasoning with Preferences over Tempo-ral, Uncertain, and Conditional Statements. Department of Pure and Applied Mathematics,University of Padova, Padova, Italy. May 2006. Supervisor: Francesca Rossi. Reviewers:Fahiem Bacchus, Thomas Schiex. Available as Technical Report UBLCS-2005-06.

1. Laurea, Mathematics,. K. Brent Venable. Solving and Learning Soft Temporal Con-straints. Department of Pure and Applied Mathematics, University of Padova, Padova,Italy. October 2001. Supervisors: Francesca Rossi, Alessandro Sperduti. Co-supervisors:Lina Khatib, Robert Morris, Paul Morris. Equivalent to A.B. plus M.S.. Recipient of the 2002

National Award for “Best Thesis on Artificial Intelligence” of the Italian Association for Artificial

Intelligence (AI*IA).

Books, book chapters, edited volumes

• Algorithmic Decision Theory - 6th International Conference, ADT 2019. S. Pekec, K.B.Venable, Editors. Lecture Notes in Computer Science 11834, Springer 2019.

• Application-Oriented Computational Social Choice - Dagstuhl Seminar 19381. U. Grandi,S. Napel, R. Niedermeier, K.B. Venable: Dagstuhl Reports 9(9): 45-65 (2019).

• Value alignment via tractable preference distance. A. Loreggia, N. Mattei, F. Rossi andK.B. Venable. Invited chapter to appear in Artificial Intelligence Safety and Security, R.V.Yampolskiy Editor, CRC Press.

• An introduction to constraint-based temporal reasoning. R. Bartak, R.A. Morris, K.B.Venable, Morgan & Claypool Publishers. 2014.

• A short introduction to preferences: between artificial intelligence and social choice. F.Rossi, K.B. Venable, T. Walsh, Morgan & Claypool Publishers. 2011.

Journal papers 1

24. Multi-agent soft constraint aggregation via sequential voting: theoretical and experimentalresults Autonomous Agents and Multi-Agent Systems. C. Cornelio, M.S. Pini, K.B. Venable.Autonomous Agents and Multi-Agent Systems. 2019.

23. Guest Editorial: Revised Selected Papers from the AMAI 2014 Special Track on Computa-tional Social Choice. F. Rossi, K.B. Venable. Ann. Math. Artif. Intell., volume 77, issue3-4, pp 157-158, 2016.

22. Editorial for the Special Issue on Temporal Representation and Reasoning (TIME’13). C.Sanchez, K.B. Venable, E. Zimanyi. In Acta Inf., volume 53, issue 2, pp 87-88, 2016.

21. Designing Noise-minimal Rotorcraft Approach Trajectories. R.A. Morris, M. Johnson, K.B.Venable and J. Lindsey. ACM Transactions on Intelligent Systems and Technology, volume6, issue 4: pp 1-25, 2016.

20. Stability, Optimality and Manipulation in Matching Problems with Weighted Preferences.M.S. Pini, F. Rossi, K.B. Venable, T. Walsh. In Algorithms volume 6, issue 4, pp 782-804,2013.

1The author ordering policy of my research group is to follow an alphabetical order.

3 of 24

19. Local Search Approaches in Stable Matching Problems. M. Gelain, M.S. Pini, F. Rossi,K.B. Venable, T. Walsh. In Algorithms volume 6, issue 4, pp 591-617, 2013.

18. Bribery in Voting with CP-nets. N. Mattei, M.S. Pini, F. Rossi, K.B. Venable. In Annalsof Mathematics and Artificial Intelligence, volume 68, Issue 1-3, pp. 135-160, 2013.

17. Winner Determination in Voting Trees with Incomplete Preferences and Weighted Votes. J.Lang, M.S. Pini, F. Rossi, D. Salvagnin, K.B. Venable, T. Walsh. In Autonomous Agentsand Multi-Agent Systems, volume 25, issue 1, pp. 130-157, 2011.

16. Uncertainty in bipolar preference problems. S. Bistarelli, S. Pini, F. Rossi, K.B. Venable.In Journal of Experimental and Theoretical Artificial Intelligence, Taylor & Francis, volume23, issue 4, pp. 545-575, 2011.

15. Incompleteness and Incomparability in Preference Aggregation: Complexity Results. M.S.Pini, F. Rossi, K.B. Venable, T. Walsh. In Artificial Intelligence - Special Issue on Rep-resenting, Processing, and Learning Preferences: Theoretical and Practical Challenges, C.Domshlak, E. Hullermeier, S. Kaci, H. Prade Editors, volume 175, issue 7-8, pp. 1272-1289,2011.

14. Manipulation complexity and gender neutrality in stable marriage procedures. M.S. Pini,F. Rossi, K.B. Venable, T. Walsh, In Autonomous Agents and Multi-Agent Systems, volume22, issue 1, pp. 183-199, 2011.

13. Interval-valued soft constraint problems. M.S. Pini, F. Rossi, K.B. Venable, N. Wilson. InAnnals of Mathematics and Artificial Intelligence, volume 58, issues 3-4, pp. 261-298, 2010.

12. Elicitation strategies for soft constraint problems with missing preferences: Properties, al-gorithms and experimental studies. M. Gelain, M.S. Pini, F. Rossi, K.B. Venable, T. Walsh.In Artificial Intelligence, volume 174, issues 3-4,pp. 270-294, 2010.

11. From soft constraints to bipolar preferences: modeling framework and solving issues. S.Bistarelli, M.S. Pini, F. Rossi, K.B. Venable. In Journal of Experimental and TheoreticalArtificial Intelligence, volume 22, issue 2, pp. 135-158, 2010.

10. Soft constraint problems with uncontrollable variables. M.S. Pini, F. Rossi, K.B. Venable.In Journal of Experimental and Theoretical Artificial Intelligence, volume 22, issue 4, pp.269-310, 2010.

9. Dynamic Consistency of Fuzzy Conditional Temporal Problems. M. Falda, F. Rossi, K.B.Venable. In Journal of Intelligent Manufacturing, volume 21, issue 1, pp. 74-88, 2010.

8. Aggregating Partially Ordered Preferences. M.S. Pini, F. Rossi, K.B. Venable, T. Walsh.In Journal of Logic and Computation, volume 19, issue 3, pp. 475-502, 2009.

7. Preferences in Constraint Satisfaction and Optimization. F. Rossi, K.B. Venable, T. Walsh.In AI Magazine, volume 29, issue 4, pp. 58-68, 2008.

6. Comparing the notions of optimality in CP-nets, strategic games and soft constraints. K.Z.Apt, F. Rossi, K.B. Venable. In Annals of Mathematics and Artificial Intelligence, volume52, issue 1, pp. 25-54, 2008.

5. Fuzzy conditional temporal problems: Strong and weak consistency. M. Falda, F. Rossi,K.B. Venable. In Engineering Applications of Artificial Intelligence, volume 21, issue 5, pp.710-722, 2008.

4. Solving and learning a tractable class of soft temporal constraints: Theoretical and experi-mental results. L. Khatib, P.H. Morris, R. Morris, F. Rossi, A. Sperduti, K.B. Venable. InAI Communications, volume 20, issue 3, pp. 181-209, 2007.

3. Uncertainty in Soft Temporal Constraint Problems: A General Framework and Controlla-bility Algorithms for the Fuzzy case. F. Rossi, K.B. Venable, N. Yorke-Smith. In Journalof Artificial Intelligence Research, volume 27, pp. 617-674, 2006.

4 of 24

2. Hard and soft constraints for reasoning about qualitative conditional preferences. C. Domsh-lak, S. Prestwich, F. Rossi, K.B. Venable, T. Walsh. In Journal of Heuristics-Special issueon preferences, S. Bistarelli and F. Rossi Editors, volume 12, pp. 263-285, 2006.

1. Aggregating preferences cannot be fair. F. Rossi, K.B. Venable, T. Walsh. IntelligenzaArtificiale, volume 2, issue 1, pp. 30-38, 2005.

Conference Papers

74. Heuristic Strategies in Uncertain Approval Voting Environments. J. Scheuerman, J. L.Harman, N. Mattei, K.B. Venable. In Proceedings of the 19th International Conference onAutonomous Agents and Multiagent Systems, AAMAS’20. IFAAMAS, 1993-1995.

73. Sequential Voting in Multi-agent Soft Constraint Aggregation. C. Cornelio, M.S. Pini, F.Rossi, K.B. Venable. In Proceedings of the 19th International Conference on AutonomousAgents and Multiagent Systems, AAMAS’20. IFAAMAS, 2131-2133.

72. Learning Preferences in a Cognitive Decision Model. T. Rahgooy, K.B. Venable. In Pro-ceedings of Human Brain and Artificial Intelligence - First International Workshop, HBAI2019, Held in Conjunction with IJCAI 2019. Communications in Computer and InformationScience 1072, Springer 2019, 181-194.

71. Heuristics and Voting Behavior in Multi-Winner Approval Voting. J. Scheuerman, J. Har-man, N. Mattei, N. and K.B. Venable In Proceedings of the Society for Judgement andDecision Making Annual Conference. SJDM-FABBS, 2019.

70. On the Distance Between CP-nets. A. Loreggia, N. Mattei, F. Rossi and K.B. Venable. InProceedings of the International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2018). Stockholm, July 10-15, 2018.

69. Preferences and Ethical Principles in Decision Making. A. Loreggia, N. Mattei, F. Rossiand K. B. Venable. In Proceedings of the AAAI Spring Symposium 2018 on AI and Society.Stanford University, CA, USA, March 26-28, 2018.

68. Decision Making over Combinatorially Structured Domains. A Martin and K.B. Venable.Doctoral Student Abstract in Proceedings of the Thirtysecond AAAI Conference on ArtificialIntelligence (AAAI 2018). New Orleans, LA, USA, Feb 2-7, 2018.

67. Preferences and Ethical Principles in Decision Making. A. Loreggia, N. Mattei, F. Rossiand K.B. Venable. In Proceedings of the AAAI/ACM Conference on AI, Ethics and Society(AIES 2018), New Orleans, LA, USA, Feb 1-3, 2018.

66. Compact Preference Representation via Fuzzy Constraints in Stable Matching Problem.M.S. Pini, F. Rossi, and K.B. Venable. In Proceedings of the 5th International Conferenceof Algorithmic Decision Theory (ADT 2017). Springer International Publishing, pp. 333-338, 2017.

65. Computational modeling of auditory spatial attention. E. Golob, K.B. Venable, J. Scheuer-man, M.T. Anderson. In Proceedings of the 39th Annual Meeting of the Cognitive ScienceSociety (COGSCI 2017). Computational Foundations of Cognition, pp. 2114 - 2119, 2017.

64. A Local Search Approach for Incomplete Soft Constraint Problems: Experimental Resultson Meeting Scheduling Problems. M. Gelain, M.S. Pini, F. Rossi, K.B. Venable and T.Walsh. In Proceedings of the 14th International Conference of Integration of AI and ORTechniques in Constraint Programming (CPAIOR 2017). Springer lncs 10335, pp. 403-418,2017.

63. Web-Content Personalization for Resilience and Risk Communication. K.B. Venable, L.Edington, P. Riser, X. Wang, A. Parker, M. Finucane. In Proceedings of the 2017 Gulf ofMexico Oil Spill and Ecosystem Science Conference (GOMOSES 2017).

5 of 24

62. Modeling Auditory Spatial Attention with Constraints. E. Golob, K.B. Venable, M. Ander-son, J. Benzell and J. Scheuerman. In Proceedings of the 4th International Workshop onArtificial Intelligence and Cognition (AIC 2016), CEUR 1895, pp. 59-72, 2016.

61. Embedding Ethical Principles in Collective Decision Support Systems. J. Greene, F. Rossi,J. Tasioulas, K.B. Venable and B. Williams. In Proceedings of the 13th AAAI Conferenceon Artificial Intelligence (AAAI2016), AAAI Press, 2016.

60. Controlling Elections by Replacing Candidates or Votes. A. Loreggia, N. Narodytska, F.Rossi, K. B. Venable, T. Walsh. In Proceedings of the 14th International Conference onAutonomous Agents and Multiagent Systems (AAMAS2015), IFAAMAS, 2015.

59. Reasoning with PCP-nets in a Multi-agent Context. Cristina Cornelio, Umberto Grandi,Judy Goldsmith, Nicholas Mattei, Francesca Rossi, K. Brent Venable. In Proceedings ofthe 14th International Conference on Autonomous Agents and Multiagent Systems (AA-MAS2015), IFAAMAS, 2015.

58. Automated Design of Quiet Trajectories Using Land Use Models. R.A. Morris, M. Johnson,K.B. Venable. In Proceedings of the 71st American Helicopter Society Annual Forum &Technology Display, Acoustics Session. AHS International, 2015.

57. Stable Matching problems with Soft Constraints. M.S. Pini, F. Rossi, K.B. Venable. ShortPaper in Proceedings of the 13th International Conference on Autonomous Agents and Mul-tiagent Systems (AAMAS 2014), IFAAMAS, 2014. (Acceptance Rate: 23.0%.)

56. Updates and Uncertainty in CP-Nets. C. Cornelio, J. Goldsmith, N. Mattei, F. Rossi,K.B. Venable. In Proceedings of the 26th Australasian Conference on Advances in ArtificialIntelligence (AI 2013), Springer LNCS 8272, pp. 301-312, 2013.

55. Restricted Manipulation in Iterative Voting: Condorcet Efficiency and Borda Score. U.Grandi, A. Loreggia, F. Rossi, K. B. Venable, T. Walsh. In Proceedings of the Third Inter-national Conference on Algorithmic Decision Theory (ADT 2013). Springer, LNCS 8176,pp. 181-192, 2013.

54. Bribery in Voting With Soft Constraints. M.S. Pini, F. Rossi, K.B. Venable. In Proceedingsof the Twenty-seventh AAAI conference on Artificial Intelligence (AAAI 2013). AAAIPress, 2013. (Acceptance Rate: 29.0%.)

53. A Framework for Aggregating Influenced CP-Nets and its Resistance to Bribery. A. Maran,N. Maudet, M.S. Pini, F. Rossi, K.B. Venable In Proceeding of the Twenty-seventh AAAIconference on Artificial Intelligence (AAAI 2013). AAAI Press, 2013. (Acceptance Rate:

29.0%.)

52. Resistance to bribery when aggregating soft constraints: complexity results. M. S. Pini, F.Rossi, K.B. Venable, Short Paper in Proceedings of the 12th International Conference on Au-tonomous Agents and Multiagent Systems (AAMAS 2013), IFAAMAS, 2013. (Acceptance

Rate: 22.0%.)

51. Local Search for Designing Noise-Minimal Rotorcraft Approach Trajectories. R.A. Morris,K.B. Venable, M Pegoraro, J. Lindsay. In The Twenty-Fourth Conference on InnovativeApplications of Artificial Intelligence (IAAI 2012), AAAI Press, 2012.

50. Influence and aggregation of preferences over combinatorial domains. N. Maudet, M.S. Pini,F. Rossi, K.B. Venable. Short Paper in Proceedings of the 11th International Conference onAutonomous Agents and Multiagent Systems (AAMAS 2012) IFAAMAS, 2012. (Acceptance

Rate: 22.9%.)

49. Bribery in voting over combinatorial domains is easy. N. Mattei, M.S. Pini, F. Rossi, K.B.Venable. Short Paper in Proceedings of the 11th International Conference on AutonomousAgents and Multiagent Systems (AAMAS 2012). IFAAMAS, 2012. (Acceptance Rate:

22.9%.)

6 of 24

48. Automated Design of Noise-Minimal, Safe Rotorcraft Trajectories. J. Lindsay, R.A. Morris,K.B. Venable. in Proceedings of the 68th American Helicopter Society Annual Forum &Technology Display, Acoustics Session. AHS International, 2012.

47. Simulation to Support Local Search in Trajectory Optimization Planning. J. Lindsay, R.A.Morris, K.B. Venable. In Proceedings of the 2012 IEEE Aerospace Conference. Big Sky,Montana (USA). IEEE, 2012.

46. Temporal preferences. K.B. Venable. Invited contribution in Proceedings of the 18th Inter-national Symposium on Temporal Representation and Reasoning (TIME 2011), CPS, 2011.

45. Weights in stable marriage problems increase manipulation opportunities. M.S. Pini, F.Rossi, K.B. Venable, Toby Walsh. Proceedings of the Thirteenth conference on TheoreticalAspects of Rationality and Knowledge (TARK XIII), recipient of the “best poster award”,pp. 200-204, ACM, 2011.

44. The next best solution. R. Brafman, E. Pilotto, F. Rossi, D. Salvagnin, K.B. Venable,T. Walsh. In Proceedings of the Nectar Program (new scientific and technical advances inresearch) at the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11), pp.1537-1540, AAAI Press, 2011. (Acceptance Rate: 24.8%.)

43. Multi-agent soft constraint aggregation via sequential voting. G. Dalla Pozza, M.S. Pini,F. Rossi, K.B. Venable. In Proceedings of the Twentysecond International Joint Conferenceon Artificial Intelligence (IJCAI 2011), pp. 172-177, IJCAI/AAAI, 2011. (Acceptance Rate

30.2%.)

42. Stability and Optimality in matching problems with weighted preferences. M.S. Pini, F.Rossi, K.B. Venable, T. Walsh. In Proceedings of the 3rd International Conference on Agentsand Artificial Intelligence (ICAART 2011), Volume 2 - Agents, pp. 45-53, SciTePress, 2011.

41. Procedural fairness in stable marriage problems. M. Gelain, M.S. Pini, F. Rossi, K.B. Ven-able, T. Walsh. In Proceedings of the Tenth International Joint Conference on AutonomousAgents and Multi Agent Systems (AAMAS-11), pp. 1209-1210, IFAAMAS, 2011. (Accep-

tance rate 22%.)

40. Possible and necessary winners in voting trees: majority graphs vs. profiles. M.S. Pini, F.Rossi, K.B. Venable, T. Walsh. In Proceedings of the Tenth International Joint Conferenceon Autonomous Agents and Multi Agent Systems (AAMAS-11), pp. 311-318, IFAAMAS,2011. (Acceptance rate 22%.)

39. Multi-agent Soft Constraint Aggregation - A Sequential Approach. M.S. Pini, F. Rossi,K.B. Venable, T. Walsh. In Proceedings of the 3rd International Conference on Agentsand Artificial Intelligence (ICAART 2011), Volume 1 - Artificial Intelligence, pp. 277-282,SciTePress, 2011.

38. A local search approach to solve incomplete fuzzy and weighted CSPs. M. Gelain, M.S.Pini, F. Rossi, K.B. Venable, T. Walsh. In Proceedings of the 3rd International Conferenceon Agents and Artificial Intelligence (ICAART 2011), Volume 1 - Artificial Intelligence, pp.582-585, SciTePress, 2011.

37. Male optimal and unique stable marriages with partially ordered preferences. M. Gelain,M.S. Pini, F. Rossi, K.B. Venable, T. Walsh. In Proceedings of the International Workshopon Collaborative Agents - REsearch and development (CARE 2009/2010), Springer LNAI6066, 2011.

36. Male optimality and uniqueness in stable marriage problems with partial orders. M. Gelain,M.S. Pini, F. Rossi, K.B. Venable, T. Walsh. In Proceedings of the Ninth International JointConference on Autonomous Agents and Multi Agent Systems (AAMAS-10), pp. 1387-1388.IFAAMAS 2010.(Acceptance rate 24%.)

7 of 24

35. Local search algorithms on the Stable Marriage Problem: Experimental Studies. M. Gelain,M.S. Pini, F. Rossi, K.B. Venable, T. Walsh, In Proceedings the Nineteenth European Con-ference on Artificial Intelligence (ECAI 2010), pp. 1085-1086, Frontiers in Artificial Intel-ligence and Applications vol.215, IOS Press, 2010.(Acceptance rate 21%.)

34. Finding the Next Solution in Constraint- and Preference-Based Knowledge RepresentationFormalisms. R.I. Brafman, F. Rossi, D. Salvagnin, K.B. Venable, T. Walsh. In Proceedingsof he Twelfth International Conference on the Principles of Knowledge Representation andReasoning (KR2010)), AAAI Press, 2010.

33. Local Search for Stable Marriage Problems with Ties and Incomplete Lists. M. Gelain,M.S. Pini, F. Rossi, K.B. Venable, T. Walsh. in Proceedings of the Eleventh Pacific RimInternational Conference on Artificial Intelligence (PRICAI 2010), LNCS 6230, pp. 64-75,Springer, 2010.

32. Manipulation and gender neutrality in stable marriage procedures. M.S. Pini, F. Rossi,K.B. Venable, T. Walsh. In Proceedings of the Eighth International Joint Conference on Au-tonomous Agents and Multi Agent Systems (AAMAS-09), pp. 665-672. IFAAMAS 2009.(Ac-

ceptance rate 22%.)

31. Compact Preference Representation in Stable Marriage Problems. E. Pilotto, F. Rossi, K.B.Venable, T. Walsh. In Proceedings of the First International Conference on AlgorithmicDecision Theory (ADT 2009), LNCS 5783, pp. 390-401, Springer, 2009.

30. Preference Aggregation over Restricted Ballot Languages: Sincerity and Strategy-Proofness.U. Endriss, M.S. Pini, F. Rossi, K.B. Venable. In Proceedings of the Twentyfirst Inter-national Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 122-127, IJCAI,2009.(Acceptance rate 26%.)

29. Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithmsand Experimental Studies. M. Gelain, M.S. Pini, F. Rossi, K.B. Venable, T. Walsh. InProceedings of the Fourteenth International Conference on Principles and Practice of Con-straint Programming (CP 2008), LNCS 5202, pp. 402-417, Springer, 2008. (Acceptance rate

41%.)

28. Robust Solutions in Unstable Optimization Problems. M.S. Pini, F. Rossi, K.B. Venable, R.Dechter. In Post-Proceedings of the Annual ERCIM Workshop on Constraint Solving andConstraint Logic Programming (CSCLP 2008), pp. 116-131, LNCS 5655, Springer, 2008.

27. Dealing with Incomplete Agents’ Preferences and an Uncertain Agenda in Group DecisionMaking via Sequential Majority Voting, M.S. Pini, F. Rossi, K.B. Venable, T. Walsh. InProceedings of the Tenth International Conference on the Principles of Knowledge Repre-sentation and Reasoning (KR2008), pp. 571-578, AAAI Press, 2008.

26. Dealing with Incomplete Preferences in Soft Constraint Problems. M. Gelain, M.S. Pini, F.Rossi, K.B. Venable. In Proceedings of the Thirteenth International Conference on Princi-ples and Practice of Constraint Programming (CP 2007), LNCS 4741, pp. 286-300, Springer,2007.(Acceptance rate for long papers 30.1%.)

25. Uncertainty in Bipolar Preference Problems. S. Bistarelli, M.S. Pini, F. Rossi, K.B. Venable.In Proceedings of the Thirteenth International Conference on Principles and Practice ofConstraint Programming (CP 2007), LNCS 4741, pp. 782-789, Springer, 2007.(Acceptance

rate 40%.)

24. Strong Controllability of Disjunctive Temporal Problems with Uncertainty. B. Peintner,K.B. Venable, N. Yorke-Smith. In Proceedings of the Thirteenth International Conferenceon Principles and Practice of Constraint Programming (CP 2007), LNCS 4741, pp. 856-863,Springer, 2007.(Acceptance rate 40%.)

8 of 24

23. A comparison of notions of optimality in Soft Constraints and Graphical Games. K.Z.Apt, F. Rossi, K.B. Venable. In Post-Proceedings of the Annual ERCIM Workshop onConstraint Solving and Constraint Logic Programming (CSCLP 2007) , LNCS 5129, pag.1-16, Springer, 2007.

22. Winner Determination in Sequential Majority Voting. J.Lang, M.S. Pini, F. Rossi, K.B.Venable, T. Walsh. In Proceedings of the 20th International Joint Conference on ArtificialIntelligence (IJCAI 2007), pp. 1372-1377, Morgan Kaufmann, 2007.(Acceptance rate 35%.)

21. Incompleteness and Incomparability in Preference Aggregation. M.S. Pini, F. Rossi, K.B.Venable, T. Walsh. In Proceedings of the 20th International Joint Conference on ArtificialIntelligence (IJCAI 2007), pp. 1464-1469, Morgan Kaufmann, 2007.(Acceptance rate 35%.)

20. Bipolar Preference Problems: Framework, Properties and Solving techniques. S. Bistarelli,M.S. Pini, F. Rossi, K.B. Venable. In Post-Proceedings of Annual ERCIM Workshop onConstraint Solving and Constraint Logic Programming (CSCLP 2006), LNCS 4651, pag.78-92, Springer, 2006.

19. Computing possible and necessary winners from incomplete partially-ordered preferences.M.S. Pini, F. Rossi, K.B. Venable, T. Walsh. In Proceedings the Fifteenth European Con-ference on Artificial Intelligence (ECAI 2006), Frontiers in Artificial Intelligence and Ap-plications vol.215, pp. 767-768, IOS Press, 2006.(Acceptance rate 26%.)

18. Bipolar preference problems. S. Bistarelli, M.S. Pini, F. Rossi, K.B. Venable. In Proceed-ings the Fifteenth European Conference on Artificial Intelligence (ECAI 2006), Frontiers inArtificial Intelligence and Applications vol.215, pp. 705-706, IOS Press, 2006.(Acceptance

rate 26%.)

17. Strategic voting when aggregating partially ordered preferences, M.S. Pini, F. Rossi, K.B.Venable, and T. Walsh, In Proceedings of the Fifth International Joint Conference on Au-tonomous Agents and Multi Agent Systems (AAMAS-06), pp. 685-687. ACM, 2006.(Accep-

tance rate 23%.)

16. Uncertainty in Soft Constraints Problems. M.S. Pini, F. Rossi, K.B. Venable. In Proceedingsof International Conference on Intelligent Agents, Web Technologies and Internet Commerce(IAWTIC 2005), pp. 583-589, IEEE, 2005.

15. CP-nets and Nash equilibria. K.Z. Apt, F. Rossi, K.B. Venable. In Proceedings of 3rd In-ternational Conference, on Computational Intelligence, Robotics and Autonomous Systems(CIRAS’05), pp. 13-19, Elsevier, 2005.

14. Disjunctive temporal planning with uncertainty. K.B. Venable, N. Yorke-Smith. In Pro-ceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005),pp. 1721-1722, Morgan Kaufmann, 2005.(Acceptance rate 18%.)

13. Aggregating partially ordered preferences: possibility and impossibility results. M.S. Pini,F. Rossi, K.B. Venable, T. Walsh. In Proceedings of the 10th Conference on TheoreticalAspects of Rationality and Knowledge (TARK-2005), pp. 193-206, ACM Digital Library,2005.

12. Constraint-based Preferential Optimization. S. Prestwich, F. Rossi, K.B. Venable, T. Walsh.In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI 2005), pp.461-466, AAAI Press / MIT Press, 2005.(Acceptance rate 28%.)

11. Possibility Theory for Reasoning about Uncertain Soft Constraints. M. S. Pini, F. Rossi,K.B. Venable. In Proceedings of the 8th European Conference on Symbolic and QuantitativeApproaches to Reasoning with Uncertainty, (ECSQARU 2005), LNCS 3571, pp. 800-811,Springer, 2005.

10. Softly Constrained CP-nets. S. Prestwich, F. Rossi, K.B. Venable, T. Walsh. In Proceedingsof the Tenth International Conference on Principles and Practice of Constraint Programming(CP 2004), LNCS 3258, p. 806, Springer, 2004.(Acceptance rate 39%.)

9 of 24

9. Controllability of Soft Temporal Constraint Problems. F. Rossi, K.B. Venable, N. Yorke-Smith. In Proceedings of the Tenth International Conference on Principles and Practice ofConstraint Programming (CP 2004), LNCS 3258, pp. 588-603, Springer, 2004.(Acceptance

rate 39%.)

8. mCP nets: representing and reasoning with preferences of multiple agents. F. Rossi, K.B.Venable, T. Walsh. In Proceedings of the 19th National Conference on Artificial Intelligence(AAAI 2004), pp. 729-734, AAAI Press / MIT Press, 2004.(Acceptance rate 27%.)

7. Alpha-dynamic controllability of Simple Temporal Problems with Preferences and Uncer-tainty. K.B. Venable. In Proceedings of the 9th International Conference on Principles andPractice of Constraint Programming (CP 2003), LNCS 2833, p. 999, Springer 2003.(Accep-

tance rate 45%.)

6. Soft Constraints for Handling Temporal Preferences. F. Rossi, K.B. Venable. RecontresFrancophones sur la Logique Flue et ses Applications Cepadues Editions, 2003.

5. Temporal Reasoning with Preferences and Uncertainty. F.Rossi, K.B. Venable, N. Yorke-Smith. In Proceedings of the 18th International Joint Conference on Artificial Intelligence(IJCAI 2003), pp. 1385, Morgan Kaufmann, 2003.(Acceptance rate 21%.)

4. Tractable Pareto Optimization of Temporal Preferences. L. Khatib, P. Morris, R. Mor-ris, K.B. Venable. In Proceedings of the 18th International Joint Conference on ArtificialIntelligence (IJCAI 2003), pp. 1289-1294, Morgan Kaufmann, 2003.(Acceptance rate 21%.)

3. Reasoning about soft constraints and conditional preferences: complexity results and ap-proximation techniques. C. Domshlak, F. Rossi, K.B. Venable, T. Walsh. In Proceedings ofthe 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), pp.215-220,Morgan Kaufmann, 2003.(Acceptance rate 21%.)

2. Ceteris Paribus Statements Represented as Soft Constraints. K.B. Venable. In Proceedingsof the Eighth International Conference on Principles and Practice of Constraint Program-ming (CP 2002), LNCS 2470, p. 779, Springer, 2002.(Acceptance rate 41%.)

1. Solving and Learning Soft Temporal Constraints: an experimental study. F. Rossi, K.B.Venable, A. Sperduti, L. Khatib, P. Morris, R. Morris. In Proceedings of the Eighth Interna-tional Conference on Principles and Practice of Constraint Programming (CP 2002), LNCS2470, pp. 249-263, Springer, 2002.(Acceptance rate 41%.)

Recent workshop papers

13. Metric Learning for Value Alignment. A. Loreggia, N. Mattei, F. Rossi, K.B. Venable. InProceedings of the Workshop on Artificial Intelligence Safety AISafety@IJCAI 2019 . CEURWorkshop Proceedings 2419, 2019.

12. Modeling Deliberation Over Combinatorially-Structured Domains: Similarity,Attractionand Compromise Effects. A. Martin and K.B. Venable. In Behavioral EC, in conjunctionwith the 20th ACM Conference on Economics and Computation (ACM EC 2019) as part ofthe 2019 ACM Federated Computing Research Conference (ACM FCRC 2019), ACM, 2019.

11. Heuristics in Multi-Winner Approval Voting. J. E. Scheuerman, J. Harman, N. Mattei, K.B. Venable. In Behavioral EC, in conjunction with the 20th ACM Conference on Economicsand Computation (ACM EC 2019) as part of the 2019 ACM Federated Computing ResearchConference (ACM FCRC 2019), ACM, 2019.

10. Modeling Deliberation over Combinatorially Structured Domains: Similarity, Attractionand Compromise effects . A. Martin and K.B. Venable. AI and Computational Psychol-ogy: Theories, Algorithms and Applications (CompPsy 2018). Co-located with IJCAI 2018,Stockholm, Sweden, August 2018.

10 of 24

9. Deconfliction of Spacecraft Communication Schedules with Preference Optimization. J.Bresina, M. Dortenzio, P. Morris and K.B. Venable. 11th Multidisciplinary Workshop onPreferences Handling (MPREF 2018) co-located with AAAI 2018. New Orleans, LA, USA,Feb 3, 2018.

8. A Notion of Distance Between CP-nets. A. Loreggia, N. Mattei, F. Rossi and K.B. Venable.11th Multidisciplinary Workshop on Preferences Handling (MPREF 2018) co-located withAAAI 2018. New Orleans, LA, USA, Feb 3, 2018.

7. Decision Making Over Combinatorially-Structured Domains. A. Martin and K.B Venable.11th Multidisciplinary Workshop on Preferences Handling (MPREF 2018) co-located withAAAI 2018. New Orleans, LA, USA, Feb 3, 2018.

6. Modeling spatial auditory attention: handling equiprobable attended locations. J. Scheuer-man, K. B. Venable, M.T. Anderson, E.J. Golob. IJCAI 2017 Workshop on Cognition andArtificial Intelligence for Human-Centred Design. Melbourne, Australia, Aug 21, 2017

5. A notion of distance between CP-nets. A. Loreggia, N. Mattei, F. Rossi and K.B. Ven-able. Fifth International Workshop on Graph Structures for Knowledge Representation andreasoning (GKR 2017), co-located with IJCAI 2017. Melbourne, Australia, Aug 21, 2017

4. Modeling Ethical Theories Compactly. A. Loreggia, F. Rossi, K.B. Venable. AAAI 2017Third International Workshop on AI, Ethics and Society (AIES 2018). San Francisco, Feb7th, 2017.

3. Companion-Based Ambient Robust Intelligence (CARING). B. Dorr, L. Galescu, E.J. Golob,K.B. Venable, Y. Wilks. AAAI 2015 Workshop on Artificial Intelligence Applied to AssistiveTechnologies and Smart Environments. Austin, Texas, USA, January 2015.

2. Optimizing Rotorcraft Approach Trajectories with Acoustic and Land Use Models. R.Morris, M. Johnson, K.B. Venable. AAAI 2015 Workshop on AI for Transportation (WAIT-15): Advice, Interactivity and Actor Modelling. Austin, Texas, USA, January 2015.

1. Flexibility meets Variability: A multi-agent constraint based approach for incorporatingrenewables into the power grid. X. Jiang, R. Mettu, G. Parker and K.B. Venable. AAAI2015 Workshop on Computational Sustainability. Austin, Texas, USA, January 2015.

Internationalcollabora-tions • Past collaborations:

– Krzysztof R. Apt, CWI and University of Amsterdam, the Netherlands. Visited in Oc-tober 2007. Research Topic: Relationship between different optimality notions comingfrom game theory and AI-based preference models.

– Stefano Bistarelli, University of Perugia, Italy. Research Topic: Extension of softconstraints to bipolar preferences.

– Carmel Domshlak, Technion, Israel. Research Topic: Comparison and hybridizationbetween two AI-based preference frameworks (CP-nets and Soft Constraints).

– Steve Prestwich, 4C (Cork Constraint Computation Center) and UCC, Ireland. Visitedin October 2003. Research Topic: Comparison and hybridization between two AI-basedpreference frameworks (CP-nets and Soft Constraints).

– Alessandro Sperduti, University of Padova, Italy. Research Topic: Learning temporalpreferences.

– Nic Wilson, 4C (Cork Constraint Computation Center) and UCC, Ireland. Visited inJune 2007. Research Topic: Uncertainty in preferences.

– Ronen I. Brafmam, Ben Gurion University, Israel. Guest in Padova in April 2009.Research Topic: Complexity of finding more than one optimal solution in AI-basedpreference models (CP-nets and Soft Constraints).

11 of 24

– Rina Dechter, UC Irvine, USA. Research Topic: Uncertainty in preferences and tree-shaped approximations of soft constraint problems and their induced preference order-ings.

– Bart Peintner (SRI International) and Neil Yorke-Smith (SRI International and Amer-ican University of Beirut), USA. Research Topics: Temporal constraint problems withuncertainty.

– Nicolas Maudet, Paris Dauphine, France. Research Topics: Modeling preference influ-ence in AI-based preference frameworks.

– Mead Allison, Tulane University, USA; Mike Blum, Tulane University, USA; Robert A.Morris, NASA Ames, USA. Research Topic Artificial intelligence techniques for sensorplacement of water sensors in the Gulf.

– Robert A. Morris, NASA Ames, USA; Matt Johnson, IHMC, USA and John BresinaNASA Ames, USA. Research Topic: Temporal Preferences and AI techniques for theRotorcraft Noise Minimization Problem.

– Geoff Parker and Jiang Xiayue, Businesses School, Tulane University; Ram Mettu,Tulane University, USA. Research Topics: Multi-agent modeling for energy demand inthe the smart grid.

– Bill Clancey, IHMC, USA, guest at Tulane June 2014; James Allen, IHMC, USA,guest at Tulane February 2014; Mike Burke, Business School, Tulane University. Re-search Topics Embedding preferences in workpractice simulations and deployment ofcomputational social choice methods on PIM architectures in the context of oil spillsmitigation.

– Ulle Endriss, University of Amsterdam, The Netherlands. Guest at Tulane in March2014. Research Topics: Manipulation and sincerity in the context of AI-based preferencemodels; CP-nets.

– Andrea Loreggia, University of Padova, Italy; Nina Narodytska, University of Toronto,Canada; Francesca Rossi, University of Padova, Italy, Toby Walsh, NICTA and UNSW,Australia, guest at Tulane April 2013. Research Topics: Manipulation and Control invoting rules.

– Olanike Ola Orie, Department of Linguistics, Tulane University. Research Topics:Optimality and Preference classification Methods for Arabic.

– Francesco Santini, CNR, Italy, guest at Tulane Dec. 2013. Research Topics: Manipu-lation in argumentation frameworks.

• Current collaborations:

– Tim Carruthers and Mead Allison, The Water Institute, New Orleans, USA. ResearchTopic: Harnessing Intelligent Systems and Big Data to Develop and Deliver WaterResilience Tools to End Users.

– F. Rossi, IBM Watson and University of Padova; J. Greene, Harvard University, J.Tasioulas, King’s College London, B. Williams, MIT, Andrea Loreggia, University ofPadova, and Nick Mattei, IBM Watson. Research Topic: Embedding Ethical Principlesin Collective Decision Support Systems.

– Matt D’Ortenzio, John Bresina and Paul Morris, NASA Ames, USA. Research Topic:Jigsaw: Software for Critical Event Deconfliction for Interplanetary NanoSat Missions

– Melissa Finucane, Rand Corporation, within the GoMRI GRCR consortium: ResearchTopic: Artificial intelligence and behavioral decision research methods to develop aprofile-based web tool.

– Ed Golob, Tulane University, USA and Paul Colombo, Tulane University, USA. Re-search Topic: Computational models of auditory spatial attention.

– Mike Mislove, Tulane University, USA and Ellis Fenske, Tulane University, USA. Re-search Topic Soft Constraints in the context of anonymity.

12 of 24

– Paul H. Morris, NASA, Ames, California, USA. Research Topic: Controllability anddispatchability of temporal networks.

– Cristina Cornelio, University of Padova, Italy; Francesca Rossi, University of Padova,Italy; Umberto Grandi, University of Padova, Italy; Nick Mattei, NICTA, Australiaand Judy Goldsmith, University of Kentucky, USA. Research Topics: Probabilisticconditional preferences.

– Toby Walsh, NICTA and UNSW, Australia, and Francesca Rossi, University of Padova,Italy. Research Topics: Learning preferences from matchings.

– Jerome Lang, CNRS, and Francesca Rossi, University of Padova, Italy. Research Top-ics: Minimum distance rationizability of voting rules over combinatorial domains.

– Jeff Lockman, Bjorn Kahrs and Carola Wenk, Tulane University. Research Topic:Developing Smart Manipulators.

– Yorick Wilks, IHMC, USA, guest at Tulane February 2014; Bonnie Dorr, IHMC, USA;Ed Golob, Tulane University; Sam Philips Tampa VA, FL, USA; Jan Jasiewicz, TampaVA. Research Topics: Artificial Companions for ALS Patients.

– Niranjan Suri, IHMC, USA. Research Topics: Embedding preferences for informationranking in the context of tactical information services.

Invited talksand Seminars

• What will Artificial Intelligence Bring? Discussing the Advent and Consequences of Super-human Intelligence. Public panel moderator and organizer. Opening event of AAAI/ACMConference on AI, Ethics and Society (AIES 2018). Tulane University, New Orleans, LA,USA. Feb 1, 2018.

• Dagstuhl Seminar on Computational Social Choice: Theory and Applications. June 7-June12, 2015. Schloss Dagstuhl, Germany.

• Preferences in Artificial Intelligence Women in Tech TU seminars. To be held Spring, 2015.Tulane University, New Orleans, LA, USA.

• What is Artificial Intelligence? Invited talk in the context of the “Hour of Code” initiativeat Metairie Park Country Day High School. Metairie, Lousiana, December 3, 2014.

• What is Artificial Intelligence? Invited talk in the context of the “Hour of Code” initiativeat Metairie Park Country Day Lower School. Metairie, Lousiana, December 12, 2014.

• The Future of Computational Social Choice (Invited panelist). Fifth International Workshopon Computational Social Choice (COMSOC 2014), CMU, Pittsburgh, PA, USA, June 2014.

• Detection of clandestine activity in massive data: beliefs, preferences and anomalies, (InvitedPresentation). Workshop on Detecting Manipulation of Collectively Produced Resources,Annapolis Junction, MD, USA, May 22, 2014. $1000 consultation fee.

• Dagstuhl Seminar on Preference Learning. March 2014. Schloss Dagstuhl, Germany.

• Compact Preference Models in Single and Multi Agent Settings. University of Texas atDallas. January 31th, 2014. USA.

• Compact Preference Models in Single and Multi Agent Settings. School of Science andEngineering Lunch Seminars. Tulane University. January 17th, 2013. USA.

• Preferences in Intelligent Systems. SSE Honor Society Seminar Series. November 15, 2012.Tulane University, New Orleans, USA.

• Feminine aspects of Artificial Intelligence: flexibility, adaptability and consensus in societiesof artificial agents. “Women and Science”, special track on “Excellence in Women”, ScuolaGalileiana di Studi Superiori, October 17th, 2011, Padova, Italy.

13 of 24

• Temporal Preferences. TIME 2011, September 2011 Lubeck, Germany.

• Compact Preference Models in Single and Multi Agent Settings. Forum for Artificial Intel-ligence, UT Austin, April 2011.

• Computational social choice. NASA Ames Research Center, March 2011, Moffett Field (CA)USA.

• Artificial Intelligence: Intelligent Systems and their Applications. From health care to Space.Scuola Galileiana di Studi Superiori, November 2010, Padova, Italy.

• Dagstuhl Seminar on Computational Issues in Social Choice. October 2007. Schloss Dagstuhl,Germany.

• Dagstuhl Seminar on Preferences: Specification, Inference, Applications. June 2004. SchlossDagstuhl, Germany.

• Solving and Learning Soft Temporal Constraints, Invited talk for the 2002 National Awardfor “Best Thesis on Artificial Intelligence” of the Italian Association for Artificial Intelligence(AI*IA), October 2002, Perugia, Italy.

• Temporal reasoning with preferences, ITC-Irst Research Center, October 2001, Trento, Italy.

Awards andDistinctions

• National Award for “Best Thesis on Artificial Intelligence” of the Italian Association forArtificial Intelligence (AI*IA), 2002.

• PentaCLE: a constraint and learning company. Finalist of the 2004 StartCup Competitionfor Innovative Business Ideas of the Cariparo Foundation.

Tutorials

• F. Rossi, K.B. Venable and T. Walsh. Voting rules in Artificial Intelligence. Twenty-NinthAAAI Conference on Artificial Intelligence (AAAI-15), January 25-29, 2015, Austin, Texas,USA.

• R. Bartak, R.A. Morris and K.B. Venable. Constraint-Based Temporal Reasoning. Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), January 25-29, 2015, Austin,Texas, USA.

• F. Rossi, K.B. Venable and T. Walsh. Social Choice. 20th International Conference onPrinciples and Practice of Constraint Programming (CP 2014). Lyon, France, September8-12, 2014.

• R. Bartak, R.A. Morris and K.B. Venable. An Introduction to Constraint-Based TemporalReasoning. 24th International Conference on Automated Planning and Scheduling (ICAPS2014). Portsmouth, NH, USA, June 2014.

• F. Rossi, K.B. Venable and T. Walsh A Short Introduction to Preferences: Between ArtificialIntelligence and Social Choice, 20th European Conference in Artificial Intelligence, August27-31 2012, Montepellier, France.

• F. Rossi and K.B. Venable. Soft Constraints and Temporal Preferences. Fifteenth Interna-tional Conference on Automated Planning and Scheduling (ICAPS 2005), June 5-10 2005,Monterey, California, USA.

• F. Rossi, K.B. Venable and Toby Walsh. Preferences: Modeling Frameworks, ReasoningTools, and Multi-Agent Scenarios. 21st International Joint Conference on Artificial Intelli-gence (IJCAI 2009), Pasadena, California, USA, July 11-17, 2009.

14 of 24

C++, JAVA, ML, PHP, MySQL.ProgrammingLanguages

TeachingMy teaching activity has been carried out in the areas of Fundamentals of Computer Science,Programming Languages, Algorithms, Data Bases, Operating Systems, Networks, and ArtificialIntelligence. I have taught courses for the Undergraduate Curriculum in Computer Science at Uni-versity of Padova and Tulane University, and for the Undergraduate Curriculum in Mathematics,Biotechnologies and the Graduate Curriculum in Computer Science of University of Padova. Be-low I give a detailed list of the courses I have been involved with, organized in academic years.

• [Academic Year 2005-2006].

– Exercises of Advanced Programming Languages (ML and Lisp). Undergraduate Cur-riculum in Computer Science. Average number of students: 70.

– Constraint Programming. Assistant. Graduate Curriculum in Computer Science. Av-erage number of students: 10.


– Exercises of Programming (C++). Undergraduate Curriculum in Computer Science.Average number of students: 170.

– Exercises of Advanced Programming Languages (ML and Lisp). Undergraduate Cur-riculum in Computer Science. Average number of students: 70.

– Fundamentals of Computer Science. Undergraduate Curriculum in Biotechnology. Av-erage number of students: 50.

– Temporal Reasoning. Graduate Curriculum in Computer Science. Average number ofstudents: 12.


– Exercises of Programming (C++). Undergraduate Curriculum in Computer Science.Average number of students: 172.

– Co-instructor of Algorithms 2 (Algorithms for Graphs). Undergraduate Curriculum inComputer Science. Average number of students: 130.

– Fundamentals of Computer science. Undergraduate Curriculum in Biotechnology. Av-erage number of students: 47.



– Exercises of Algorithms. Undergraduate Curriculum in Computer Science. Averagenumber of students: 130.

– Exercises of Advanced Programming Languages (ML and Lisp). Graduate Curriculumin Computer Science. Average number of students: 20.


• [Academic Year 2009-2010]. During this Academic year my teaching load was reduceddue to maternity leave.

– Database Laboratory. Undergraduate Curriculum in Computer Science. Average num-ber of students: 130.


15 of 24




– Temporal Reasoning. Graduate Curriculum in Computer Science. Average number ofstudents: 18. With guest lectures from Prof. Robert Morris, NASA Ames.






– Fall semester: Introduction to Discrete Mathematics (MATH 2170) . Number of Stu-dents 22. Undergraduate Curriculum in Mathematics, Tulane University.

– Spring Semester: Introduction to Systems (CMPS 2300). Number of Students: 5.Undergraduate Curriculum in Computer Science, Tulane University.


– Fall semester:

∗ Introduction to Artificial Intelligence (CMPS 3140-6140). Number of Students 12.Undergraduate Curriculum in Computer Science, Tulane University.

∗ Independent Study (CMPS 4910): Ambient Obstacle Avoidance. Number of Stu-dents: 1. Undergraduate Curriculum in Computer Science, Tulane University.

– Spring Semester:

∗ Introduction to Systems (CMPS 2300). Number of Students: 12. UndergraduateCurriculum in Computer Science, Tulane University.

∗ Independent Study (CMPS 4910): Advanced Machine Learning. Number of Stu-dents: 1. Undergraduate Curriculum in Computer Science, Tulane University.

• [Academic Year 2014-2015]

– Fall semester

∗ Fall semester: Introduction to Artificial Intelligence (CMPS 3140-6140). Numberof Students 12. Undergraduate Curriculum in Computer Science, Tulane Univer-sity.

∗ Independent Study: Machine Learning (CMPS 4910). Number of Students: 1.Undergraduate Curriculum in Computer Science, Tulane University.

– Spring Semester: Introduction to Machine Learning (CMPS 3240-6240). Undergradu-ate Curriculum in Computer Science, Tulane University.


– Fall semester

∗ Fall semester: Introduction to Artificial Intelligence (CMPS 3140-6140). Numberof Students 11. Undergraduate Curriculum in Computer Science, Tulane Univer-sity.

∗ Fall semester: Artificial Intelligence (CMPS 4620-6620). Number of Students 7.Graduate Curriculum in Computer Science, Tulane University.

16 of 24

∗ Independent Study: Applied AI Preference Models (CMPS 4910). Number ofStudents: 1. Undergraduate Curriculum in Computer Science, Tulane University.

– Spring Semester

∗ Independent Study: Ethics for Artificial Intelligence (CMPS 4910). Number ofStudents: 1. Undergraduate Curriculum in Computer Science, Tulane University.

∗ Independent Study: AI and Human Computer Interfaces (CMPS 7980). Number ofStudents: 1. Undergraduate Curriculum in Computer Science, Tulane University.


– Fall semester

∗ Multi-Agent Systems (CMPS 3120-6120). Number of Students 6. Graduate Cur-riculum in Computer Science, Tulane University.

∗ Independent Study: Ethics for Artificial Agents II (CMPS 4920). Number ofStudents: 1. Undergraduate Curriculum in Computer Science, Tulane University.

– Spring Semester

∗ Intro to Artificial Intelligence (CMPS 3140-6140). Number of Students: 23. Un-dergraduate Curriculum in Computer Science, Tulane University.

∗ Independent Study: Computational Neuroscience (CMPS 4920). Number of Stu-dents: 1. Undergraduate Curriculum in Computer Science, Tulane University.


– Fall semester


– Spring Semester



– Fall semester


– Spring Semester



– Spring Semester

∗ Research Methods in Intelligent Systems and Robotics (ISC 6529). Number ofStudents: 5. Graduate Curriculum in Intelligent Systems and Robotics, UWF-IHMC.

• Graduate Short Courses

– Computational Social Choice, in Collaboration with Francesca Rossi and Maria Sil-via Pini, Ph.D. program in Computer Science of University of Padova and Bologna.Department of Pure and Applied Mathematics, University of Padova (Italy), Spring,2012.

– Constraint Programming and Constraint Logic programming. III Summer School onLogic Programming and Computational Logics organized by COMPULOG Americasand ALP. New Mexico State University Las Cruces (NM,USA), July 24-27, 2008.

– Preferences. In collaboration with Toby Walsh (NICTA-UNSW). Ph.D. program inComputer Science of University of Padova and Bologna. Department of Pure andApplied Mathematics, University of Padova (Italy), May 5-9, 2008.

17 of 24

Studentadvising I have been involved in the supervision of two Ph.D. students, several Master students and several

undergraduate students. Below I give a complete list of the students I have collaborated with andthe of the topics that were jointly considered.

• University of Padova

– Ph.D. students

∗ Maria Silvia Pini, University of Padova Computational Mathematics Ph.D. Rea-soning with Preferences and Uncertainty. Co-supervised with Francesca Rossi.Defended March 2007. Currently Assistant Professor at the Department of Com-puter Science and Engineering of the University of Padova.

∗ Mirco Gelain, University of Padova Ph.D. student. Reasoning with Incompleteand Imprecise Preferences. Co-supervised with Francesca Rossi. Will defend May2011. Currently network administrator for a municipality.

– M.S. Students

∗ Riccardo Ferro, University of Padova Computer Science M.S., 2010. Constraintprogramming Techniques for the Rotorcraft Noise Minimization Problem. ExternalReviewer: John Bresina (NASA) Ames. Evaluation: Original, high quality work,9/10 points.

∗ Michele Volpato, University of Padova Computer Science M.S., 2010. Weak anddynamic controllability of temporal problems with uncertainty and disjunctions.External Reviewer: Robert Morris (NASA) Ames. Evaluation: Original, highquality work, 9/10 points.

∗ Daniele Brognara, University of Padova Computer Science M.S., 2007. AdaptiveConsistency in Temporal Constraint Problems. External Reviewer: Fabio Aiolli(Univ. of Padova). Evaluation: Original, high quality work, 9/10 points.

∗ Alberto Amaran. Embedding influences in compactly represented preferences.

∗ Giorgio Dalla Pozza,University of Padova Computer Science M.S., 2010. Aggre-gating compactly represented preferences. Co-supervised with Francesca Rossi.

∗ Enrico Pilotto, University of Padova Computer Science M.S., 2009. Compact pref-erence representation in stable marriage problems. Co-supervised with FrancescaRossi.

∗ Mirco Gelain, University of Padova Computer Science M.S., 2007. Reasoning withincomplete and imprecise preferences. Co-supervised with Francesca Rossi.

∗ Mauro Riva and Stefano Antonello, University of Padova Computer Science M.S.,2006. Modeling and solving problems with preferences and uncontrollable vari-ables. Co-supervised with Francesca Rossi.

∗ Luca Girotto, University of Padova Computer Science M.S., 2006. Modeling andsolving bipolar preference problems. Co-supervised with Francesca Rossi.

∗ Elisabetta Bortolamiol, University of Padova Mathematics M.S. 2003. Optimiza-tion and dominance testing in multi-Agent systems using qualitative preferences.Co-supervised with Francesca Rossi.

∗ Claudio Muttinelli, University of Padova Mathematics M.S. 2002. Controllabilityof temporal constraint problems with preference and uncertainty. Co-supervisedwith Francesca Rossi.

The Undergraduate Curriculum in Computer Science of the University of Padova re-quires students to work for approximately three months in a company of their choice(industry internship) or to take an academic internship. In the first case, the studentis supervised by a tutor belonging to the company, responsible for the actual contentof the thesis, and by a faculty member responsible for the formal correctness of thethesis and its presentation. In the second case, the student is supervised by two facultymembers. The undergraduate thesis is the summary of the work done by the studentduring the internship.

18 of 24

– Undergraduate Students

∗ With internship in industry

· Gina Verbanac, University of Padova Computer Science A.B., 2010. ExtremeProgramming methodologies in the Steel industry. In collaboration with SystecAutomation.

· Paolo Carollo, University of Padova Computer Science A.B., 2009. Documenthandling via Java technologies. In collaboration with Luce S.a.s.

· Andrea Zaupa, University of Padova Computer Science A.B., 2009. SinSms:Newsletter Short Message System in Ruby on Rails. In collaboration withWindnet s.r.l.

· Luca Bragante, University of Padova Computer Science A.B., 2009., HostingServices for Ruby on Rails. In collaboration with Windnet s.r.l.

· Andrea De Boni, University of Padova Computer Science A.B., 2009. A webapplication for automatic activity summary generation. In collaboration withIKS s.r.l.

· Martina Graziotto, University of Padova Computer Science A.B., 2009. Afront-end web application based on a distributed J2EE architecture. In collab-oration with Finvweb.

· Luca Scatigno. University of Padova Computer Science A.B., 2009. An anal-ysis and projection system for web marketing investments. In collaborationwith Diginess.

· Alberto Guiotto, University of Padova Computer Science A.B., 2008. Webgeninterfaces generation templates within the automatic generation system Egen.In collaboration with Soluzioni Software s.r.l.

· Carlo Sarto, University of Padova Computer Science A.B., 2008. A messagingapplication for tracking technologies. In collaboration with Qascom.

· Alberto Marchiori. University of Padova Computer Science A.B., 2008. Newfunctionalities and integrations to a company report generation system. Incollaboration with Arcadia Consulting.

· Giacomo Gomiero. University of Padova Computer Science A.B., 2007. Auto-matic report generation. In collaboration with Sirav.

· Alessandro Trombetta. Developing a graphical interface with C# in the .Netenvironment and in Mono environment. In collaboration with Zucchetti.

∗ With academic internship

· Marco Pegoraro. Incorporating turns in noise minimizing landing trajectoriesfor rotorcrafts. In collaboration with NASA Ames.

· Christian Cardin. Developing a tool for archaeologists. In collaboration withProf. Paolo Kirschner (University of Padova, Dept. of Archeology)

· Davide Navarro. Magenda: allowing for preferences and preference aggregationin a meeting scheduling tool. Co-supervised with Francesca Rossi.

• Tulane University

– Graduate

∗ Abiola Akanni: AI and Ethics. PhD advisor.

∗ Jaelle Scheuerman: Preferences in computational cognitive models. PhD advisor.

∗ Andrea Martin: Preferences in AI and Marketing. PhD advisor.

∗ Taher Raghooy: Preference Learning. PhD Advisor.

∗ Ellis Fenske (PhD in Mathematics candidate): Soft constraints in the context ofanonymity and security. (Member of oral examination panel.)

∗ Max T. Anderson (PhD in Neuroscience): Spatial Attention of the auditory system(Member of the PhD committee.)

– Undergraduate

19 of 24

∗ Cassie Wang. Web-content personalization for resilience and risk communication.CS Capstone project.

∗ Ethan Bogart. Communication scheduling for swarms of small spacecraft. CSCapstone project.

∗ Peter Riser. Web-content personalization for resilience and risk communication.CS Capstone project.

∗ Laura Edington. Web-content personalization for resilience and risk communica-tion. CS Capstone project.

∗ Kyle Bogosian. AI and Ethics. Directed research project.

∗ Nick Teal. Implementation of a constraint-based computational model for auditoryspatial attention. Co-supervised: Ed Golob, UTSA.

∗ Vaughan Cordell. AI and Ethics. CS Capstone project.

∗ Alex Gain. Matching problems. CS Capstone Project and Mathematics HonorThesis. Co-supervised with Michael Joyce, Mathematics Department, Tulane.

∗ Olivia Cabello-Gorsch. Web-content personalization for resilience and risk com-munication. CS Capstone project.

∗ Cody Licorish. Web-content personalization for resilience and risk communication.CS Capstone project.

∗ Duc Ho: Machine learning for homology group prediction. CS Capstone project.Co-supervised with: Rafal Komendarczyk, Mathematics Department, Tulane.

∗ Ben Wisialowski: Eliciting preferences with BCIs. CS Capstone project. Co-supervised: Ed Golob, Neuroscience, Tulane.

∗ Mateo Rodriguez: Implementation and testing of a probabilistic roadmap approachfor quiet rotorcraft landing trajectories. CS Capstone project.

∗ Brenan Keller: Extracting preferences for concert recommendation. CS Capstoneproject.

∗ Kyle Buschkoetter: Implementation of a constraint-based computational model forauditory spatial attention. Co-supervised: Ed Golob, Tulane.

∗ Brenan Keller: Ambient Obstacle Avoidance. Co-supervised: Matt Johnson,IHMC.

∗ Taylor Shrake: Implementation of Configuration Space and a Local Search Algo-rithm for the computational model of the development of fitting in humans.

∗ Tyler Schlichenmeyer: Machine learning techniques for autofocusing for medicalimaging. CS capstone project. Co-supervised with: Carola Wenk, Tulane.

∗ Skye Anderson: Evaluating Arabic Authorship Identification Techniques. (ThirdReader).

∗ Tess Jacobson: Probabilistic Conditional Networks. Supervisor for the Math Se-nior Seminar.

∗ Richard Metcalf: Security Games. Supervisor for the Math Senior Seminar.

• UWF

– Graduate

∗ Andrea Martin: Preferences in AI and Marketing. PhD advisor.

∗ Taher Raghooy: Preference Learning. PhD Advisor.

∗ Connor Tate: Environmental Intelligent Systems. PhD Co-Advisor. Attention ofthe auditory system (Member of the PhD committee.)

ResearchGrants andCooperationagreements

• Current

20 of 24

– A Declarative Learning based Programming Framework for the Integration of DomainKnowledge and Statistical Learning. P.I. Parisa Kordjamshidi, Tulane University andIHMC. ONR. Venable role: Co-PI. Award Amount: $1,800,000. Start date: February27, 2019.

– Auditory Spatial Attention Control. PI: Ed Golob. Co-I: K. Brent Venable, PaulColombo. NIH R01 Program. Award Period: 10/01/2015-10/01 Award Amount:$1,500,000. Award Period: 10/01/2015-09/30/2020.

• Past:

– Student Program of the Second Conference on Artificial Intelligent (AI), Ethics andSociety (AIES 2019). P.I. NSF. Award Amount: $15,000. Start date: December 20,2019.

– Consortium for Resilient Gulf Communities. PI: Melissa Finucane, RAND. GoMRIResearch Consortia. Funded for: $8,000,000. Venable role: Lead investigator forTulane University CS. Tulane CS requested budget: $497,328. Start date: January 1,2015.

– Embedding Ethical Principles in Collective Decision Support Systems. Future of LifeInstitute. PI: F. Rossi, University of Padova and IBM. Co-Is: J. Greene, HarvardUniversity, J. Tasioulas, King’s College London, K.B. Venable, IHMC and B. Williams,MIT. Award Amount: $275,000. Award Period: 1/12/2015-30/11/2018.

– Public Panel on AI, Ethics and Society. Tulane Carol Lavin Bernick Faculty Grant.P.I.: K. B. Venable. Award Amount: $6,000. Awarded for event on 2/1/2018.

– Public Panel on AI, Ethics and Society. Tulane D.W. Mitchell Grant. P.I.: K.B.Venable. Award Amount: $6,000. Awarded for event on 2/1/2018.

– NSF CC*IIE Networking Infrastructure: Riverfront Campus Research Network. PI:Charlie MCMahon, Tulane University. Co-is: Mead Allison, K. Brent Venable andLieu Tran, Tulane University. Award amount: $497,700. Award period: 10/01/2014 –09/30/2016.

– Designing of Noise-minimal Rotorcraft Trajectories. NASA ISRDS task n. 0104 MOD04. PI: Robert A. Morris. Co-Is: Matt Johnson and Kristen Brent Venable, IHMC.FY2014: $140,000.

– University of Padova point-of-contact and responsible for the coordination and execu-tion of the NASA-University of Padova agreement for Rotorcraft Noise Reduction andTrajectory Optimization, signed November 2010.

– MIUR PRIN 20089M932N. 2008 Nationally relevant project funded by the Italian Min-istry of Research and University. Title:Innovative and multi-disciplinary approaches forconstraint and preference reasoning, PI: Francesca Rossi. Venable role: Co-I. Euros:82.587.

– MIUR PRIN 2005015491. 2005 Nationally relevant project funded by the Italian Min-istry of Research and University. Title:Constraints and Preferences, PI: FrancescaRossi. Venable role: Co-I. Euros: 113.000.

– University of Padova grant CPDA048871. Title Preferences and uncertainty in schedul-ing problems and multi-agent systems. PI: Rossi Francesca. Venable role: Co-I. Euros:26.164.

DepartmentalServices andNationalBoards

• I have been on at least two graduation boards in Computer Science at University of Padovaper year since 2006 until 2012.

• Member of Ph.D. in Computer Science National Committee at University of Perugia, Febru-ary 2011.

21 of 24

• Chair of the committee for the English Language Test for the Undergraduate Curriculumin Computer Science, University of Padova. Years 2008, 2009.

• Chair of the committee of the teaching timetable of the Undergraduate and Graduate Cur-ricula in Computer Science, University of Padova. From 2009 to 2012.

• Member of the Hiring Committee of the Tulane Computer Science Department. Year: 2014.

• Organizer of the IHMC seminars series at Tulane. Year: 2013-2014.

• Nominations Committee, School of Science and Engineering, Tulane University. (2016-2017)

• Member of the Tulane Linguistic Graduate Faculty. (2015-2019)

• Board Member of the Masters in Computational Linguistics Program. Current.

• Study abroad representative for the Tulane CS Department. (2014-2019)

• Grievance Committee, School of Science and Engineering, Tulane University. (2016-2019)

• Member of the Graduate Studies Committee, Computer Science Department, Tulane Uni-versity. (2016-2019)

• Chair of the Hiring Committee of the UWF Intelligent Systems and Robotics Program.Current.

• Chair of the Graduuate Admission Committee of the UWF Intelligent Systems and RoboticsProgram. Current.

Editorial &programcommitteeand reviewerservice

• Editorial service

– Editor of the Special Issue on Computational Social Choice and Preferences for Annalsof Mathematics and Artificial Intelligence, January 2014- present

– Editor of the Special Issue on TIME 2013 for Acta Informatica, December 2013-2016

– AI Magazine, editorial board, December 2013-present

– JAIR (Journal of Artificial Intelligence Research) editorial board, 2009-2017

• Chair

– Second ACM/AAAI International Conference on AI, Ethics and Society AIES 2019Student Program, Honolulu, Hawaii, January 27-28, 2019. Co-chair.

– ADT 2019, the 6th International Conference on Algorithmic Decision Theory, 25-27October 2019 Duke University, Durham, NC, USA. Chair.

– Dagstuhl Seminar on Computational Social Choice, Co-chair, 2019.

– First ACM/AAAI International Conference on AI, Ethics and Society, co-located withAAAI 2018, Feb 2-3, 2018, New Orleans, Public events chair, 2018

– AAAI Spring Symposium 2018 on AI and Society. To be held at Stanford Universityon March 26-28, 2018

– ICAPS (International Conference on Automated Planning and Scheduling) tutorialco-chair 2007

– SOFT’11, 11th Workshop on Preferences and Soft Constraints, in conjunction with the17th International Conference on Principles and Practice of Constraint Programming(CP’11) organizer 2011

– MPREF 2012, 6th Multidisciplinary Workshop on Advances in Preference Handling, inconjunction with the 20th European Conference on Artificial Intelligence (ECAI 2012),co-chair 2012

22 of 24

– MPREF 2013, 7th Multidisciplinary Workshop on Advances in Preference Handling,in conjunction with the 23rd International Joint Conference on Artificial Intelligence(IJCAI 2013), co-chair 2013

– MPREF 2014, 8th Multidisciplinary Workshop on Advances in Preference Handling,in conjunction with the 28th AAAI Conference on Artificial Intelligence (AAAI 2014),co-chair 2014

– MPREF 2018, 11th Multidisciplinary Workshop on Advances in Preference Handling,in conjunction with the 28th AAAI Conference on Artificial Intelligence (AAAI 2018,January 2018, New Orleans, USA ), co-chair 2017

– EXPLORE 2014, 1st Workshop on Exploring Beyond the Worst Case in ComputationalSocial Choice, in conjunction with the 13th International Conference on Autonomousand Multi-Agent Systems, co-chair 2014

– TIME 2013, 20th International Symposium on Temporal Representation and Reasoning(TIME 13) 26 - 28 September, Pensacola, FL, USA, chair 2013

– TIME 2014, 21st International Symposium on Temporal Representation and Reasoning(TIME 14) program committee, special track chair 2014

– ISAIM 2014, International Symposium on Artificial Intelligence and Mathematics, FortLauderdale, FL, USA, special track chair 2014

– ISAIM 2016, International Symposium on Artificial Intelligence and Mathematics, FortLauderdale, FL, USA, chair 2016

• Program Committee

– AAAI (National Conference on Artificial Intelligence) senior program committee mem-ber 2018, 2020, 2021

– IJCAI (International Joint Conference on Artificial Intelligence) senior program com-mittee 2011, 2015, 2016, 2017, 2018, 2020

– IJCAI program committee 2003, 2005, 2007, 2009, 2011, 2013

– AAAI (National Conference on Artificial Intelligence) program committee 2005, 2006,2007, 2008, 2013, 2014, 2015, 2016, 2017

– CILC program committee 2011

– CP (International Conference on Principles and Practice of Constraint Programming)program committee 2009, 2013, 2014

– CP doctoral program committee 2009

– AI (Australasian Joint Conference on Artificial Intelligence), program committee 2012

– SIGAI CNC (ACM SIGAI Career Network Conference), program committee 2015

– ICAART (International Conference on Agents and Artificial Intelligence), programcommittee 2013, 2014, 2015

– CP reviewer 2003, 2004, 2005, 2006, 2007, 2008, 2010

– ICAPS reviewer 2006

– ECAI (European Conference on Artificial Intelligence) reviewer 2004, 2006, 2008, 2010,2012, 2018

– FLAIRS (Florida Artificial Intelligence Research Society Conference) program com-mittee 2009, 2010

– AAMAS (International Conference on Autonomous Agents and Multiagent Systems)program committee 2010, 2011, 2012, 2013, 2017, 2018, 2019

– KR (Principles of Knowledge Representation and Reasoning) reviewer 2008, programcommittee 2012

• Reviewer

23 of 24

– Reviewer for the following journals and post-proceedings: JAIR, AIJ (Artificial Intel-ligence), CI (Computational Intelligence), IJAIT (International Journal on ArtificialIntelligence Tools), RAC (Recent Advances in Constraints), Constraints, Transactionof Fuzzy Systems, Theoretical Computers Science

– AAAI auxiliary reviewer 2004

– CILC (Italian Conference on Computational Logics) reviewer 2008

– FLAIRS program committee 2009

– AAMAS reviewer 2007, 2008, 2009

– COMSOC (International Workshop on Computational Social Choice) reviewer 2008,2010, 2014

– Proposal reviewer for the Superior Council of the National Fund for Scientific andTechnological Development (FONDECYT) of the Chilean government, 2011

– Proposal reviewer for the Netherlands Organisation for Scientific Research (NWO),2011.

Personal

• Born July 1975, Dallas (TX) USA. Grew up between Asolo (Italy) and Dallas (Texas, USA).Son: Robert Arthur Chimenti Venable, Daughter: Jasmine Skye Venable Bradley.

• Interests: Vegan, member of the PETA (People for Ethical Treatment of Animals) vanguardsociety since 2007.

• Languages: English, Italian, Latin and Ancient Greek.

24 of 24

kristen brent venable

Documents