Foreword

Information is often stated in a bipolar way, that is, a distinction is oftenmade between positive and negative aspects. This especially concerns the waypieces of knowledge, as well as preferences, are expressed. An obvious exampleis the opposition between beliefs and disbeliefs that are graded on appropriatescales where positive and negative values are explicitly accounted for. Similarityand dissimilarity (which may differ from nonsimilarity) offer another illustration ofthe idea of bipolarity. Argumentation is by nature bipolar, since one distinguishesbetween arguments in favor of and arguments against a debatable claim or choice.

More generally, positive information often corresponds to a collection of piecesof empirical evidence, such as observed cases, examples, to situations that canbe encountered for sure, to states that are explicitly permitted, to recommendedchoices, to what an agent likes or desires. Negative information often reflectsa priori restrictions on the possible states of the world, which may be stated underthe form of generic rules (maybe with some exceptions). It points out impossiblesituations, counterexamples, forbidden states, potentially bad choices, or yet whatan agent dislikes or rejects.

Positive information and negative information, when they are simultaneouslypresent, should often be handled separately and even differently. This separateprocessing matters in a variety of information processing and reasoning tasks, suchas multicriteria decision, preference modeling, belief revision, information fusion,automated reasoning, deontic reasoning, argumentative reasoning, or learning. Akey issue is a proper representation of the different forms of bipolar information.Different logical or uncertainty-oriented representation frameworks make sense inthat perspective.

The contents of this double special issue on bipolar representations are theresult of several seminars involving researchers in artificial intelligence, decisiontheory, cognitive psychology, and philosophy. These meetings were held at variousplaces in France (Foix, October 23–25, 2003; Arras, March 25–27, 2004; Le Fossat,Carla-Bayle, October 14–16, 2004).

The special issue is organized in two main parts, each of them correspondingto one issue of the journal. The content of the first part is devoted to cognition anddecision. The next part is devoted to reasoning and learning.

The first paper “An introduction to bipolar representations of information andpreference” identifies three forms of bipolarity, respectively termed “symmetricunivariate” (type 1), “dual bivariate” (type 2), and “asymmetric (or heterogeneous)bipolarity” (type 3).

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, VOL. 23, 863–865 (2008)C© 2008 Wiley Periodicals, Inc. Published online in Wiley InterScience(www.interscience.wiley.com). • DOI 10.1002/int.20296

864 FOREWORD

Type 1 mainly refers to the use of scales where positive values are distinguishedfrom negative ones. Type 2 bipolarity uses two independent scales (one positive, onenegative), but positive and negative strengths are computed on the basis of the samedata. This is no longer the case with type 3 bipolarity. The interest of the differenttypes of bipolarity in reasoning and decision tasks is then advocated.

The next three papers are contributions from the point of view of cognitivepsychology. The first one “An evolutionist approach to information bipolarity: Rep-resentations and affects in human cognition” (by E. Raufaste and S. Vautier) inves-tigates the psychological plausibility of the bipolarity concept and emphasizes thefact that positive and negative information are not processed by the human brain inthe same way, both for affects and for mental categories. The second paper “Bipo-larity in human reasoning and affective decision making” (by R. Da Silva Nevesand P. Livet) first shows how positive and negative information can independentlyaccount for the distinction between classical forms of reasoning and points out therelevance of the possibility theory-based representation of bipolar information of thethird type for explaining psychological findings. The last part of the paper exploresthe interest of a bipolar view of emotions in affective decision making. The thirdpaper “Two routes for bipolar information processing, and a blind spot in between”(by J.-F. Bonnefon) discusses why human reasoners sometimes fail to appropriatelydeal with information of mixed polarities, in spite of the fact that the human brainuses specific processes for handling bipolar information.

The next three papers, which close the first part of this collection on bipolarrepresentations, deal with multicriteria decision and preference modeling. The firstone, “Bipolar models in multi-criteria decision analysis: Descriptive and construc-tive approaches” (by M. Grabisch, S. Greco, and M. Pirlot) proposes approaches tomultiple criteria aggregation handling type 1 or type 2 bipolar information. The pa-per starts with a discussion of Tversky and Kahneman’s cumulative prospect theorythat already distinguishes positive and negative parts in the evaluation of a prospectand proposes an overview of more general models that include the extension of theChoquet integral with bi-capacities (that are real-valued monotonic functions whoseargument is a pair of sets) or with bipolar capacities (that return a pair of numbers).The second paper “Bipolar preference modeling in decision: A bilattice approach”(by M. Ozturk and A. Tsoukias), after a brief review of the use of bipolar scalesand the discussion of some examples, proposes a generalization of the concepts ofconcordance and discordance that refer to positive and negative reasons for prefer-ring one alternative to another, where preferences are expressed through comparisonof intervals. The third paper “Representing and reasoning with bipolar prioritizedpreferences” (by S. Kaci) provides an overview of logical representation settings(possibilistic logic, qualitative choice logic, and penalty logic) for bipolar prefer-ences of the third type, corresponding to the contrast between (flexible) constraintsand criteria modeling goals and desires.

International Journal of Intelligent Systems DOI 10.1002/int

FOREWORD 865

Acknowledgment

The work presented in this double special issue received a financial supportfrom CNRS (Specific Action AS 119 in the setting of the “Reseau ThematiquePluridisciplinaire” (RTP 11) “Information et Intelligence: Raisonner et Decider”).

Didier Dubois and Henri PradeToulouse, December 2006

International Journal of Intelligent Systems DOI 10.1002/int