contributions of scg to sdg karl lieberherr northeastern university college of computer and...

Contributions of SCG to SDG

Contributions of SCG to SDGKarl Lieberherr

Northeastern UniversityCollege of Computer and Information Science

Boston, MA joint work with

Ahmed Abdelmeged and Bryan Chadwick

Karl LieberherrNortheastern University

College of Computer and Information ScienceBoston, MA

joint work with Ahmed Abdelmeged and Bryan Chadwick

Supported by Novartis

SCG = Scientific Community Game = Specker Challenge Game

Game Motto

• Want reliable software to solve a computational problem? Design a game where the winning team will create the software you want.

• (Want to teach a STEM domain? Design a game where the winning students demonstrate superior domain knowledge.)

04/22/23 Games for SD 2

SCG

• Make software development more scientific.• Software developers– propose claims about their software– oppose claims made by others about their own

software• refute claims• strengthen claims

• claim defined by refutation protocol


Claims and Refutation Protocol

• AliceClaim: I have a program that solves inputs in domain X with quality Q and resources R. – AliceClaim(X,Q,R)

• Bob is critical. He prepares a tough input in X and gives it to Alice who applies her program. Bob refutes iff Alice achieves < Q or uses > R. – Refutation protocol


Who are Alice and Bob?

• They are avatars developed by real Alice and real Bob.

• Alice and Bob compete with 10 other avatars in a full-round robin tournament.

• Who is the winner: The avatar with the highest reputation, i.e., the strongest, not successfully opposed claims (like in a real scientific community).


What we want

• Engage software developers– let them produce software that models an

organism that fends for itself in a real virtual world while producing the software we want. Have fun. Focus them.

– let them propose claims about the software they produce. Reward them when they • defend their claims successfully or • oppose the claims of others successfully.


Opening the development approach

• Problem to be solved: Develop the best practical algorithms for solving computational problems in domain X.

• Issue: There are probably hundreds of papers on the topic with isolated implementations. What are the best practical algorithms?

• Our solution: Use the scientific community game SCG(X) with a suitably designed claims language to compare the software. The winning avatar has the best practical algorithms/software.

04/22/23 8Games for SD

Example:Independent Set

• An independent set in a graph is a set of mutually nonadjacent vertices. The problem of finding a maximum independent set in a graph, is one of most fundamental combinatorial NP-hard problems.


Example: Independent Set

• claim IndSet(n, 0.9, t(n)):– Alice can construct graphs G with at most n

vertices and she can construct a secret independent set I1 for G so that Bob, given G, size(I1) and t(n) minutes only, cannot find an independent set I2 with• size(I2) >= size(I1)*0.9.


Refutation Protocol

• Alice constructs graph G and deposits her secret independent set I1.

• Alice gives G as well as the size of I1 to Bob.• Bob has 10 minutes to construct his

independent set I2 which he gives to Alice.• Alice reveals her secret set I1.• Bob wins iff size(I2) >= size(I1)*0.9


Benefits for IBM of using SCG(X)

• Teams perform know-how retrieval and integration and maybe some research. – Participating teams try to find the best knowledge in

the area.– Claims language gives control!

• The non-opposed claims give hints about new X-specific knowledge.

• A well-tested solver for X-problems that integrates the current algorithmic knowledge in field X.


Benefits for IBM of using SCG(X)

• Also great for evaluating potential employees.


Avatars propose and oppose


CA1

CA2

CA3

CA4

egoisticAlice

egoisticBob

reputation 1000 reputation 10

CB1

CB2

opposes (1)

provides problem (2)

solves problem

not as well as she expected based on CA2 (3)WINS!LOSES

proposed claims

transfer 200

social welfare

Life of an avatar: (propose+ oppose+ provide* solve*)*

What is SCG(X)?

TeamsDesign Problem Solver

Develop SoftwareDeliver Avatar

Agent Alice Agent Bob

Administrator SCG police

I am the best

No!!

Let’s play constructive

ly04/22/23 15Games for SD

TeamAlice

TeamBob

competitive / collaborative


Avatar Alice: claim H

Avatar Bob: opposes H, refutes: providesevidence for !H

loses reputation r wins knowledge k

wins reputation rmakes public knowledge k

Disadvantages of SCG

• The game is addictive. After Bob having spent 4 hours to fix his avatar and still losing against Alice, Bob really wants to know why!

• Overhead to learn to define and participate in competitions.

• The administrator for SCG(X) must perfectly supervise the game. Includes checking the legality of X-problems.– if admin does not, cheap play– watching over the admin


How to compensatefor those disadvantages

• Warn the scholars.• Use a gentleman’s security policy: report

administrator problems, don’t exploit them to win.

• Occasionally have a non-counting “attack the administrator” competitions to find vulnerabilities in administrator.– both generic as well as X-specific vulnerabilities.


Related Work

• TopCoder


Conclusions

• SCG has many applications of potential value to IBM– Training employees in constructive domains– Software development process– Hiring– Driving innovation in constructive domains


Thank you


Software Development Governance

• Software Development Governance (SDG) is defined as:– Establishing chains of responsibility, authority and

communication to empower people within a software development organization

– Establishing measurement and control mechanisms to enable software developers, project managers and others within a software development organization to carry out their roles and responsibilities


Applications

• Develop algorithms/software for new computational domain X– Scientific Community Game Software

Development: Describe a problem domain X so that SCG(X) provides the best algorithms and their implementations for problems in X. (best within the participating scientific community)


SCG = Scientific Community Game = Specker Challenge Game

Plan• Why is it relevant, useful?

– Larger context: Open Innovation, Wikinomics– Applications: Netflix in the small, teaching

• What is it?• What is new?

– Map problem domain to “second life”, find best solution there and map it back to real life.

• What do we improve: benefits of SCG• How to use SCG• Disadvantages• Experience with current implementation• Related work • Detailed example• Conclusions


Introduction (2)

• Scientific Community Game(X) [SCG(X)]– Goal: Foster innovation and reliable software for

solving optimization problems in some domain X

• A virtual scientific community consists of virtual scholars that propose and oppose claims maximizing their reputations


Claim

• Subdomain N– subset of problems

• Confidence [0,1]• Valuation [0,1]


confidence

0

1

valuation(how wellproblems inN can be solved)

Claim

04/22/23 Games for SD 28280

1

valuationstrengthening

correct valuation

over strengthening

Hypothesis

• hypothesis by Alice: for all problems F in niche N there exists a solution J: p(F,J)• Bob opposes: F’ to Alice, Alice cannot find

J’:p(F’,J’) therefore she loses reputation.


Full Round Robin Tournaments or Swiss-Style

• Agents to play the SCG(X). Repeat a few times with feedback used to update agents.

• Within the group of participating agent, the winning agent has the– best solver for X-problems – best supported knowledge about X


What is the purpose of SCG?• The purpose of playing an SCG(X) competition is to assess

the "skills" of the agents in: – "approximating" optimization problems in domain X, – "figuring-out" the wall-clock-time-bounded approximability of

niches in domain X, – "figuring-out" hardest problems in a specific niche, and – "being-aware" of the niches in which their own solution

algorithm works best. • This multi-faceted evaluation makes SCG(X) more superior

to competitions based on benchmarks that only test the player's skills in approximating optimization problems. During SCG, players cross-test each others' skills.


How to use SCG

• Company A provides a problem domain description X and submits it to the SCG server. The game SCG(X) runs on the web (with human algorithm/software developers involved) and company A receives good, tested software and knowledge about problem domain X


From Benchmark-Driven to SCG-Driven Algorithm Development

• Hard to measure and detect what is fraud.• Instead: Design a system that needs a much

weaker “gentleman’s agreement” or none at all• The Static Benchmark Problem is ONE problem

that SCG solves. Dynamic Benchmarks• Others: crowd sourcing management, new

software development process that engages software developers and that fosters ease of evolution (e.g., good separation of concerns, …)


Problems with Static Benchmarkshttp://www.cs.kuleuven.be/~dtai/events/ASP-competition/index.shtml

Policy against special purpose solutionsThe purpose of the competition is to be as informative as

possible about strengths and weaknesses of … Submission of special purpose programs for solving certain benchmark problems falsifies the information that we get from the rankings and goes against the spirit of the competition. … the use of special purpose programs for certain benchmarks can rightfully be considered as scientific fraud.

We appeal to participants …


http://www.cs.kuleuven.be/~dtai/events/ASP-competition/index.shtml

http://www.cs.kuleuven.be/~dtai/events/ASP-competition/index.shtml

SCG-Driven Algorithm Development

• Differences to Benchmark-Driven– You don’t rank chess players by giving them a

benchmark; you let them play–We turn the algorithms into egoistic virtual

scientists that fend for themselves– social welfare: constructive knowledge based on

good algorithms


What is SCG(X)


no automationhuman plays

full automationagent plays

degree of automation used by scholar

our focus

some automationhuman plays

0 1

more applications:test constructive knowledge

transfer to reliable, efficient software

agent BobAlice

Scholars and Agents:Same rules

• Are encouraged to 1. offer results that are not easily improved.2. offer results that they can successfully

support.3. strengthen results, if possible. 4. stay active and publish new results or

oppose current results.5. become famous!


More Applications

• Special issue editors for problem domain X. publish top 15 submissions

• Professor teaching a software development class: students develop fighting agents for full-round robin tournament

• Teaching constructive topics• etc.


Soundness Theorem

• SCG is sound: The agent with the best algorithms / knowledge wins (there is no way to cheat)– best: within the group of participating agents– issues:

• Does an agent win because she is good at solving? Or good at proposing, opposing and providing? Answer: proposing, opposing and providing all reduce to solving.


Justifying benefits (1)• Benefit: competitive – collaborative• Game component: hypotheses propose-oppose :

problems provide-solve• How this game component brings the benefit– hypothesis by Alice: for all problems F in niche N there

exists a solution J: p(F,J)– Bob opposes: F’ to Alice, Alice cannot find J’:p(F’,J’)

therefore she loses reputation.– Alice lost but she now knows F’ where she cannot

achieve what she claimed. F’ was harder than what Alice expected.


Justifying benefits (2)• Benefit: competitive – collaborative• Game component: hypotheses propose-oppose :

problems provide-solve• How this game component brings the benefit– hypothesis HA by Alice: for all problems F in niche N

there exists a solution J: p(F,J)– Bob opposes by non-trivially strengthening HA to HB:

HB => HA. Alice cannot discount HB. Therefore she loses reputation.

– Alice lost but she now knows that her hypothesis HA might not be the strongest.


Benefits of SCG-driven

• Focus on understanding problem domain.–What are the niches where specialized algorithms

perform well?–What are the hard problems in a niche?

• Knowledge maintenance system• Control of niches to be explored


Reputation GainChallenging (C)

Gain for A (A supporting), Loss for A (B discounting)


How to use SCG(X)• ABB needs new ideas about how to solve

optimization problems in domain X.• Define hypothesis language for X– X-problems– hypotheses, includes protocol

• Submit hypothesis language definition to SCG server.


How to use SCG(X)• Offer prize money for winner with conditions,

e.g., performance must be at least 10% higher as performance of agent XY that ABB provides.

• 10 teams from 6 countries sign up, committing to 6 competitions. Player executables become known to other players after each competition. One team from ABB.

• The SCG server sends them the basic agent and the administrator for testing.


How to use SCG(X)

• Game histories known to all. Data mining!• First competition is at 23.59 on day 1.

Registration starts at 18.00 on same day. The competition lasts 2.5 hours.

• Repeat on days 7, 14, … 42.• The final winner is: Team Mumbai, winning

10000 Euro. Delivers source code and design document describing winning algorithm to ABB.


Benefits for ABB of using SCG(X)

• Teams perform know-how retrieval and integration and maybe some research. – Participating teams try to find the best knowledge in

the area.– Hypothesis language gives control!

• The non-discounted hypotheses give hints about new X-specific knowledge.

• A well-tested solver for X-problems that integrates the current algorithmic knowledge in field X.


GIGO: Garbage in / Garbage out

• If all agents are weak, no useful solver created.• WEAK against STRONG:– STRONG refutes a claim that is true but WEAK cannot

support it. Correct knowledge might be discounted.– STRONG strengthens a hypothesis too much that it

becomes discountable, but WEAK cannot discount it. Incorrect knowledge might be supported

– STRONG is discouraged to exploit WEAK by game rules


Experience• Used for 3 years in undergraduate Software

Development course. Prerequisites: 2 semesters of Introductory Programming, Object-Oriented Design, Discrete Structures, Theory of Computation.– Collect and integrate knowledge from prerequisite

courses, lectures, and literature. – Teach it to the agent.

• 30% of grade is allocated for agent performance in weekly competitions.


Mechanics of using current implementation

• We define X = MAX-CSP.• We produce administrator and baby agent for

X at beginning of course.• Game flow:– Agents register with administrator– After deadline, administrator tells agents when it

is their turn (1 minute) sending them all currently proposed hypotheses

– After 1 minute, agent sends back transactions.04/22/23 Games for SD 55

Mechanics of using current implementation

• 3 competitions per week. Last about 12 hours each. 75% of competitions count towards grade. 1 competition: attack the administrator.


Experience MAX-CSP

• MAX-CSP Problem Decompositions• T-Ball (one relation), Softball (several

relations, one implication tree), Baseball (several relations).

• ALL, SECRET


Stages for SECRET T-Ball

• MAXCUT – R(x,y)= x!=y– fair coin ½ – maximally biased coin ½ – semi-definite programming / eigenvalue

minimization 0.878


Stages for SECRET T-Ball

• One-in-three– R(x,y,z) = (x+y+z=1)– fair coin: 0.375– optimally biased coin: 0.444


Stages for ALL Baseball

• Propose/Oppose/Provide/Solve – based on fair coin– optimally biased coin

• correctly optimize polynomials

– correctly eliminate noise relations– correctly implement weights– …


How to model a hypothesis

• A problem space.• A discounting predicate on the problem

space.• A protocol to set the predicate through

alternating “moves” (decisions) by Alice and Bob. If the predicate becomes true, Alice wins.


How to model a hypothesis

• Proposing and challenging a hypotheses is risky: your opponent has much freedom to choose its decisions within the game rules.

• Alternating quantifiers.• Replace “exists” by agent algorithm kept by

administrator.


Hypothesis [Example]

• 1in3 example.


X = Boolean MAXCSP

• Given a sequence of Boolean constraints formulated using a set R of Boolean relations, find an assignment that maximizes the fraction of satisfied constraints.

• Niche defined by R.


1in3 niche

• Only relation 1in3 is used.• 1in3 problem F:

v1 v2 v3 v4 v51in3( v1 v2 v3)1in3( v2 v4 v5)1in3( v1 v3 v4)1in3( v3 v4 v5)secret 1 0 0 1 0

Truth Table 1in3

000 0001 1010 1011 0100 1101 0110 0111 0

Secret quality SQ = 3/4


1in3 Hypothesis• 1in3 hypothesis H proposed by Alice: exists F in

1in3 niche so that for all SBob that opponent Bob searches in time t (small constant) seconds: Quality(F,SBob) < 0.4 * Quality(F,SAlice).

• H = (niche = (1in3), AR =0.4, confidence = 0.8)• Bob has clever knowledge that Alice does not

have. He opposes the hypothesis H by challenging it using his randomized algorithm.


Bob’s clever knowledge4/9 for 1in3

• 4/9 for 1in3: For all F in 1in3 niche, exists S so that Quality(F,S) >= 0.444 * SQ.

• Proof: la(p)=3*p*(1-p)2 has the maximum 4/9. • argmax p in [0,1] la(p) = 1/3.• Without search, in PTIME.• Derandomize• Bob successfully discounts• Alice gets a hint – Was Bob just lucky?

Truth Table 1in3000 0001 1010 1011 0100 1101 0110 0111 0


1in3 Hypothesis

• Bob does not know whether 4/9 is best possible. Should check Semidefinite Programming.

• Bob only knows that the set of 1in3 problems having a solution satisfying 4/9 + eps, eps > 0, is NP-complete.


Related Work

• Renaissance mathematicians• Various benchmark based competitions• What is new?– Software that has an ego– Holistic software with introspection– Evaluating software through a game– Scientific Community Game Software

Development


Conclusions• To address a problem domain X:

– “map it to second life”: define a scientific community game for X on the web: SCG(X)

– let the game SCG(X) run a few times and choose the winner

• Benefits– Evaluates fairly, frequently, constructively and dynamically.

Encourages retrieval of state-of-the-art know-how, integration and discovery.

– Challenges humans, drives innovation, both competitive and collaborative.

– Agents point humans to what needs attention in problem solution / software.


Conclusions

• SCG(X) provides a structured process for developing software for optimization problems.

• Benefits– Social Engineering: makes it fun through game.– Fair: Only hard work makes you win.– Engage a large community on one domain X.

• Tools


contributions of scg to sdg karl lieberherr northeastern university college of computer and...

Documents

software developerslet

real alice

real bob

sdscgmake software development

maximum independent

independent set i2

real scientific community

designed claims language