Virtual Scientific Communities for Innovation Supported by Novartis and GMO

1. Virtual Scientific Communities for Innovation Supported by Novartis and GMO • Karl Lieberherr • Northeastern University • College of Computer and Information Science • Boston, MA • joint work with • Ahmed Abdelmeged and Bryan Chadwick

2. Introduction • Problems to be solved: • Optimal assembly of a system from components • hardware • software • Maximum constraint satisfaction problem (MAX-CSP) • Transporting goods minimizing energy consumption • Schedule tasks minimizing cost Innovation

3. Introduction • Solve optimization problems in a domain X (X-problems). • Find a feasible solution of good quality efficiently. • Scholars to play the Specker Challenge Game (X) [SCG(X)]. Repeat a few times. • Within the group of participating scholars, the winning scholar has the • best solver for X-problems • best supported knowledge about X Innovation

4. Introduction • Players share hypotheses about "the approximability of problems in certain niches of the problem domain" • Administrator reconciles inconsistencies between shared hypotheses => Condensing knowledge/stirring progress • Player with the strongest correct hypothesis gains reputation, the other player receives targeted feedback / gains knowledge

5. Introduction • The game is designed to exclude situations where it is impossible to give useful targeted feedback and/or it's possible to gain reputation without sharing the strongest correct hypothesis, e.g.: proposing strong obvious hypothesis, avoiding involvement with other players, mirroring, ... etc => Fair assessment

6. Benefits of SCG • Social Welfare • Supported knowledge • Hypotheses are challenged and strengthened. • Better supported knowledge comes from better algorithms and software. Innovation

7. SCG(X) Scholar Alice Scholar Bob Scholars Design Problem Solver Develop Software Deliver Agent I am the best No!! Agent Alice Agent Bob Let’s play constructively Administrator SCG police Innovation

8. our focus SCG agent robot Bob Alice degree of automation used by scholar 1 0 no automation human plays some automation human plays full automation agent plays transfer to reliable, efficient software more applications: test constructive knowledge Innovation

9. Social Engineering • Why develop problem solving software through a virtual scientific community? • 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. Innovation

10. Software Development • Software developers are knowledge integrators: Requirements, contextual information (lectures, papers), behavior of program in competition, etc. Innovation

11. Scholars and virtual Scholars! • Are encouraged to • offer results that are not easily improved. • offer results that they can successfully support. • strengthen results, if possible. • publish results of an experimental nature with an appropriate confidence level. • stay active and publish new results or oppose current results. • be well-rounded: solve posed problems and pose difficult problems for others. • become famous! Innovation

12. Agent Design • How to Design an artificial organism? • needs introspection to give it an ego. • has a basic need: maximize reputation. • has a rhythm: every round the same activity. • interacts with other agents by proposing and opposing hypotheses. • makes agent vulnerable. Innovation

13. competitive / collaborative Agent Alice: claims hypothesis H loses reputation r wins knowledge k Agent Bob: challenges H, discounts: provides evidence for !H wins reputation r makes public knowledge k Innovation

14. Definitions • A hypothesis H offered by Alice is constructively defendable by Alice against Bob if Alice supports H when Bob challenges H. • The constructive defense is determined by an interactive protocol between Alice and Bob. • A hypothesis H1 is stronger than hypothesis H2 if H1 implies H2. • Successfully opposing is a form of proposing: strengthening a hypothesis means to propose a new one. Discounting a hypothesis means to propose its complement. Innovation

15. SCG is sound • The SCG game is sound, i.e., agent Alice wins with proposed hypothesis H against opponent Bob iff • H is stronger than what Bob could constructively defend and • H is constructively defendable by Alice against Bob. Innovation

16. GIGO: Garbage in / Garbage out • If all agents are weak, no useful solver created. • WEAK against STRONG: • STRONG accepts a hypothesis that is not discountable 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. Innovation

17. What is the purpose of SCG? • The purpose of playing an SCG(X) contest is to assess the "skills" of the players 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 contests based on benchmarks that only test the player's skills in approximating optimization problems. During the game, players cross-test each others' skills. Innovation

18. 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. Innovation

19. 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. Innovation

20. 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. Innovation

21. 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. Innovation

22. Disadvantages of SCG • The game is addictive. After Bob having spent 4 hours to fix his agent 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 is possible. • watching over the admin. I am perfect Innovation

23. How to compensatefor 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” competition to find vulnerabilities in administrator. • both generic as well as X-specific vulnerabilities. Innovation

24. Experience block Innovation

25. 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. Innovation

26. Experience MAX-CSP • MAX-CSP Problem Decompositions • T-Ball (one relation), Softball (several relations, one implication tree), Baseball (several relations). • ALL, SECRET Innovation

27. 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 Innovation

28. 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 Innovation

29. 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 • … Innovation

30. The SCG(X) Game

31. How to model a scholar? • Solve problems. • Provide hard problems. • Propose hypotheses about Solve and Provide (Introspection). • Oppose hypotheses. • Strengthen hypotheses. • Challenge hypotheses. • Supported challenge failed. • Discounted challenge succeeded. Innovation

32. 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. Innovation

33. 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. Innovation

34. SQ = Quality(P, SAlice) Hypothesis • Alice’ Hypothesis: There exists a problem P in niche N of X s.t. for all solutions SBob searched by the opponent Bob in T seconds. Quality(P, SBob) < AR * Quality(P, SAlice). • Hypotheses have an associated confidence [0,1]. • Hypothesis: <N, AR, Confidence>. Innovation

35. 1in3 niche Truth Table 1in3 000 0 001 1 010 1 011 0 100 1 101 0 110 0 111 0 • Only relation 1in3 is used. • 1in3 problem P: v1 v2 v3 v4 v5 1in3( v1 v2 v3) 1in3( v2 v4 v5) 1in3( v1 v3 v4) 1in3( v3 v4 v5) secret1 0 0 1 0 Secret quality SQ = 3/4 Innovation

36. 1in3 Hypothesis • 1in3 hypothesis H proposed by Alice: exists P in 1in3 niche so that for all SBob that opponent Bob searches in time t (small constant) seconds: Quality(P,SBob) < 0.4 * Quality(P,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. Innovation

37. Bob’s clever knowledge4/9 for 1in3 • 4/9 for 1in3: For all P in 1in3 niche, exists S so that Quality(P,S) >= 0.444 * SQ. • Proof: la(p)=3*p*(1-p)2 has the maximum 4/9. • argmaxp 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 1in3 000 0 001 1 010 1 011 0 100 1 101 0 110 0 111 0 Innovation

38. End Innovation

39. Reputation Gain • Hypothesis have credibility [0,∞]. The credibility of a hypothesis is proportional to agent’s confidence in the hypothesis and agent’s reputation. • Reputation gain is proportional to the discounting factor and the hypothesis credibility. • The discounting factor [-1,1]. 1 means the hypothesis is completely discounted. Innovation

40. Discounting Factor

41. Discounting Factor • H1 = ((1in3), AR = 1.0, confidence = 1.0) • H1 proposed by Alice: exists P in 1in3 niche so that for all S that opponent Bob searches: Quality(P,S) < 1.0 * SQ. • This is a reasonable hypothesis if Alice is sure that her secret assignment is the maximum assignment when she provides a sufficiently big problem to Bob.

42. What we did not tell you so far • A game defines some configuration constants: • a maximum problem size • For example, all problems in the niche can have at most 1 million constraints. • A maximum time bound for all tasks (propose, oppose, provide, solve), e.g. 60 seconds. • An initial reputation, e.g., 100. When reputation becomes negative, agent has lost.

43. Discounting Factor: ReputationGain for Strengthening • H1 = ((1in3), AR = 1.0, confidence = 1.0) • H1 proposed by Alice: exists P in 1in3 niche so that for all S that opponent Bob searches: Quality(P,S) < 1.0 * SQ. • Bob thinks he can strengthen H1 to H2 = (MAXCSP, niche = secret ExistsForAll (1in3), AR = 0.9, confidence = 1.0). • DiscountingFactor 1.0-0.9 = 0.1. • ReputationGain for Bob = 0.1 * 1.0 * AliceReputation. • Alice gets her reputation back if she discounts H2.

44. Discounting FactorReputationGain for Discounting • H = ((1in3), AR = 0.4, confidence = 1.0) • H proposed by Alice: exists P in 1in3 niche so that for all S that opponent Bob searches: Quality(P,S) < 0.4 * SQ. • Bob knows he can discount H based on this knowledge: 4/9 for 1in3. • Let’s assume he achieves 0.45 on Alice’ problem. • DiscountingFactor 0.45 – 0.4 = 0.05 . • ReputationGain for Bob = 0.05*1.0*AliceReputation.

45. Discounting FactorReputationGain for Supporting • H = ((1in3), AR = 0.4, confidence = 1.0) • H proposed by Alice: exists P in 1in3 niche so that for all S that opponent Bob searches: Quality(P,S) < 0.4 * SQ. • Bob knows he can discount H based on this knowledge: 4/9 for 1in3. • Let’s assume he achieves 0.3 on Alice’ problem. Bob has a bug somewhere! • DiscountingFactor 0.3 – 0.4 = -0.1 • ReputationLoss for Bob = -0.1*1.0*AliceReputation.

46. Mechanism Design • The exact SCG(X) mechanism is still a work in progress. • SCG(X) mechanism must be sound: • Encourage productive behavior and discourage unproductive behavior of scientists. • The agent with best heuristics wins. Innovation

47. Tools to facilitate use of SCG(X) • Definition of X. • Generate a client-server infrastructure for playing SCG(X) on the web. • Administrator enforces SCG(X) rules: client. • Baby agents: servers. They can communicate and play an uninteresting game. • Baby agents get improved by their caregivers, register with Administrator and the game begins at midnight. Innovation

48. SCIENTIFIC COMMUNITY

49. Scholars and virtual Scholars! • Are encouraged to • offer results that are not easily improved. • offer results that they can successfully support. • quote related work and show how they improve on previous work. • publish results of an experimental nature with an appropriate confidence level. • stay active and publish new results or oppose current results. • be well-rounded: solve posed problems and pose difficult problems for others. • become famous! Innovation

50. Productive Scientific Behavior (1) • The agents propose hypotheses that are difficult to strengthen or challenge (i.e. non-trivial yet correct). Otherwise, they lose reputation to their opponents. • Offer results that cannot be easily improved. • Offer results that they can successfully support. Innovation