Supported by Novartis The Scientific Community Game: Education and Innovation Through Survival in a Virtual World of Claims Karl Lieberherr Northeastern University College of Computer and Information Science Boston, MA joint work with Ahmed Abdelmeged and Bryan Chadwick
Why Scientific Community Game (SCG) • … motives in academic publishing: – desire for recognition and respect from the people one regards as peers, – desire to have impact (on conclusions being reached, on the development of the discipline, etc. ), and – desire to participate in significant knowledgebuilding discourse. • e. g. , Scardamalia, M. , & Bereiter, C. (1994) Intro SCG 2
SCG is Bio-inspired • Virtual world of scholars based on natural selection – propose, oppose (refute and strengthen) claims – maximize reputation, weak scholars are removed. • Turn problem-solving software into virtual organisms that fend for themselves and survive in a virtual world inhabited by virtual organisms created by your peers. Intro SCG 3
SCG is a web-based implementation of Karl Popper’s science ideas • One of the greatest philosophers of science of the 20 th century. • Falsifiability or refutability is the logical possibility that an assertion could be shown false by a particular observation or physical experiment. • Error elimination (refutation), performs a similar function for science that natural selection performs for biological evolution. from Wikipedia Intro SCG 4
Comparison • Karl Popper: Conjectures and Refutations • Scientific Community Game: Claims and Refutations Intro SCG 5
Recognition in SCG • Scholars build their reputation by proposing and opposing claims, by defending their own claims and refuting or strengthening the claims of others. • The higher their reputation, the more recognition. Intro SCG 6
Impact in SCG • Second-order environment – what one scholar does in adapting, changes the environment so that others must readapt. • Developing novel techniques to find superior solutions, challenges others to catch up. Intro SCG 7
Knowledge-Building Discourse in SCG • Communication or debate. • Refutation protocol defines the structure of the debate and who wins. Claims are defined through a refutation protocol. • Knowledge-building: – claims that have been defended predominantly are candidates for truth – claims that have been refuted predominantly are probably false. Intro SCG 8
Goals of SCG • Put knowledge-building discourse on the web giving participants the option to gain recognition and to have impact. • Focus the discourse through precise definition of claims with refutation protocols. • Make knowledge building discourse fun and educational from the high school to the advanced research level. SCG = Scientific Community Game = Specker Challenge Game Intro SCG 9
What do we mean by science? • Science consists of the formulation and testing of hypotheses based on observational evidence. • Ours: Science consists of the formulation and testing of constructive claims based on observational evidence. Construction is computable. Intro SCG 10
What do we mean by Scientific Method • Hypothetico-deductive method: Formulate a hypothesis in a form that could conceivably be falsified by a test on observable data. • Ours: Formulate a constructive claim in a form that could conceivably be falsified by a test using a protocol. The refutation protocol is part of the claim to make very explicit when refutation is successful. Intro SCG 11
SCG claim examples • SCG Claim – Algorithmic. Claim • solve problems of kind D with quality q and resource r • have polynomial time algorithm to solve problems of kind D with quality q – Mathematical. Claim • for all x in X exists y in Y: predicate(x, y) – Software. Claim • solve problems of kind D with maintainability m • you cannot break into a system of kind D using resource r
SCG claim examples – Financial. Claim • if you pay me k dollars (option premium) today, I will promise to buy q shares of stock S up to day d at price p (strike price). Purpose: insurance. – Experimental. Claim • If I am given raw materials x in X, I can produce product y in Y of quality q and using resources at most r.
Tartaglia against Fior 1535 Tartaglia was famed for his algebraic solution of cubic equations which was published in Cardan's Ars Magna. Intro SCG 14
Outline • Introduction – Popper Science, Renaissance History: Tartaglia and Fior • Definition of SCG – Example (Highest safe rung) • • • Applications: Teaching, Software Development, Research Claims with secrets and other protocol variants Output of SCG, Equilibrium Advantages and Disadvantages Conclusions Intro SCG 15
Definition of SCG: Domain • • Problem: Set Solution: Set valid: relation(Problem, Solution) quality: function(Problem, Solution)->[0. . 1] Intro SCG 16
Claim(Domain) makes predictions about the future • Problems: Powerset(Domain. Problem) • q: Quality = [0, 1] • r: Resource = N+ = positive integer Alice claims to have a technique to solve problems in Problems with at least quality q and using at most resources r. Intro SCG 17
Implied Protocol of Claim(Domain) • Alice claims (problems, q, r), Bob refutes • Bob provides problem prob in Claim. Problems. • Alice solves problem prob providing sol in Domain. Solution. • check: valid(prob, sol) and quality(prob, sol)>=q and sol. resource<=r. • sol. resource returns Alice’ resource consumption to solve problem prob. Karl Popper: Only hypotheses capable of clashing with observation reports are allowed to count as scientific. Intro SCG 18
![Claim • Problems: subset of problems • quality in [0, 1] 1 quality (how](https://present5.com/presentation/1d2ef6421dc2764617c33faeecd2e22c/image-19.jpg "Claim • Problems: subset of problems • quality in [0, 1] 1 quality (how")
Claim • Problems: subset of problems • quality in [0, 1] 1 quality (how well problems in Problems can be solved) 0 Intro SCG 19
Claim over strengthening 1 correct valuation quality strengthening 0 Intro SCG 20
Bio-inspired computing: Virtual World of SCG-Avatar • SCG-Avatar (Claim(Domain)) – State: Reputation = positive rational number – Activity • propose new claims • oppose claims of others – refute claim(Problems, q, r) – strengthen claim(Problems, q’, r’), q’>q or r’<r • Reputation gain: refute others’ claims and defend own claims (counter refutation attempts) • Reputation loss: unsuccessful refutation of other’s claim and refutation of own claims Intro SCG 21
Tournament Intro SCG 1. round-robin 2. Swiss-style 3. elimination 1. single 2. double 22
![Summary of SCG Definitions Domain Problem Solution valid(Problem, Solution) quality(Problem, Solution) →[0, 1] Claim(Domain)](https://present5.com/presentation/1d2ef6421dc2764617c33faeecd2e22c/image-23.jpg "Summary of SCG Definitions Domain Problem Solution valid(Problem, Solution) quality(Problem, Solution) →[0, 1] Claim(Domain)")
Summary of SCG Definitions Domain Problem Solution valid(Problem, Solution) quality(Problem, Solution) →[0, 1] Claim(Domain) Problems: Power. Set(Domain. Problem) q: Quality = [0, 1] r: Resource = N+ Rules of the Scientific Community: propose and oppose, be an active scholar, rules for reputation accumulation. Tournaments Intro SCG 23
Highest Safe Rung • You are doing stress-testing on various models of glass jars to determine the height from which they can be dropped and still not break. The setup for this experiment, on a particular type of jar, is as follows. Intro SCG 24
Highest Safe Rung Bob Alice You have a ladder with n rungs, and you want to find the highest rung from which you can drop a copy of the jar and not have it break. We call this the highest safe rung. You have a fixed ``budget'' of k > 0 jars. Only two identical bottles to determine highest safe rung Intro SCG 25
Highest Safe Rung Bob Alice HSR(9, 2) ≤ 4 Only two identical bottles to determine highest safe rung Intro SCG I doubt it: refutation attempt! Alice constructs decision tree T of depth 4 and gives it to Bob. He checks whether T is valid. Bob wins if he finds a flaw. 26
x Highest Safe Rung Decision Tree HSR(10, 2)=5 3 no yes y 1 z 6 u 0 2 highest safe rung 4 9 1 2 3 5 7 4 9 5 8 6 7 Intro SCG 8 27
Formal: HSR • Domain: – Problem: (n, k), k <= n. – Solution: Decision tree to determine highest safe rung. – quality(problem, solution): depth of decision tree / number of rungs – valid(problem, solution): at most k left branches, . . . Intro SCG 28
Formal: HSR • Claim(Domain): – Alice claims ({(25, 2)}, 9/25, 5 seconds) • {(25, 2)}: set of problems (singleton) • 9/25: quality • 5 seconds: resource • Refutation Protocol: – Bob refutes: only one problem: (25, 2) – Alice: solves problem by providing decision tree t. – predicate: t is a valid decision tree for (25, 2) of depth 9 Intro SCG 29
SCG(HSR) Karl Lieberherr 3/17/2018 SCG(HSR) 30
Overview • Showing Scientific Community game in action as a board game. • Want to play the game in class. 3/17/2018 SCG(HSR) 31
Highest Safe Rung • You are doing stress-testing on various models of glass jars to determine the height from which they can be dropped and still not break. The setup for this experiment, on a particular type of jar, is as follows. 3/17/2018 SCG(HSR) 32
Highest Safe Rung Bob Alice You have a ladder with n rungs, and you want to find the highest rung from which you can drop a copy of the jar and not have it break. We call this the highest safe rung. You have a fixed ``budget'' of k > 0 jars. Only two identical bottles to determine highest safe rung (k=2) 3/17/2018 SCG(HSR) 33
Highest Safe Rung Bob Alice HSR(9, 2) ≤ 4 Only two identical bottles to determine highest safe rung 3/17/2018 SCG(HSR) I doubt it: refutation attempt! Alice constructs decision tree T of depth 4 and gives it to Bob. He checks whether T is valid. Bob wins if he finds a flaw. 34
SCG Scenario • Interactions between scholars Alice and Bob. Admin Nina gives grade to performance of Alice and Bob. 3/17/2018 SCG(HSR) 35
HSR(n, k) ≤ q • There exists a valid decision tree DT-HSR(n, k) of depth q to solve HSR(n, k) so that for all ladders with n rungs and for all secret rungs s, the decision tree DT-HSR(n, k) correctly identifies s. 3/17/2018 SCG(HSR) 36
x Linear Search: HSR(4, 1)=3 no yes y z 1 u 0 highest safe rung 2 1 3 2 3 depth is 3 3/17/2018 SCG(HSR) 37
x no yes Binary Search: HSR(4, 2)=2 y z 2 u 1 0 3/17/2018 highest safe rung 3 1 2 3 SCG(HSR) 38
Pos. HSR Use Case: HSR(n, k) <= q • Name: HSR • Participating actors: Alice, Bob and Nina. • Entry condition: n, k, q are given; k<=n, q<=n, refuter defined: Bob. • Flow of events 3/17/2018 SCG(HSR) 39
Pos. HSR Use Case (continued) • Flow of events – Alice claims HSR(n, k)<=q. – Bob tries to refute. Bob asks for program/algorithm for (n, k) (Provide. Problem). – Alice provides program/algorithm (Solve. Problem). – Bob/Nina check correctness of program/algorithm. – Nina gives grade based on whether program/algorithm is correct and of predicted quality. 3/17/2018 SCG(HSR) 40
Pos. HSR Use Case (continued) • Exit condition: winner and loser are determined. • Quality requirements: programming language, computational model: decision tree 3/17/2018 SCG(HSR) 41
Neg. HSR Use Case: HSR(n, k) > q • Name: HSR-neg • Participating actors: Alice, Bob and Nina. • Entry condition: n, k, q are given; k<=n, q<=n, refuter defined: Bob. • Flow of events 3/17/2018 SCG(HSR) 42
Neg. HSR Use Case (continued) • Flow of events – Alice claims HSR(n, k)>q. – Bob tries to refute. Alice asks for program/algorithm for (n, k) (Provide. Problem). – Bob provides program/algorithm (Solve. Problem). – Alice/Nina check correctness of program/algorithm. If depth of decision tree is <= q, refutation is successful. – Nina gives grade based on whether program/algorithm is correct and of predicted quality. 3/17/2018 SCG(HSR) 43
Neg. HSR Use Case (continued) • Exit condition: winner and loser are determined. • Quality requirements: programming language, computational model: decision tree 3/17/2018 SCG(HSR) 44
HSR(x, 1)<=x-1 x no yes y z 1 u 0 highest safe rung 2 1 3 2 x-1 depth is x-1 x-2 3/17/2018 x-1 SCG(HSR) 45
Bob has the following claims • • HSR(4, 1)<=4 HSR(9, 2)<=3 HSR(8, 3)<=3 HSR(4, 2)<=2 HSR(11, 2)<=4 HSR(12, 2)<=4 Alice makes a decision for each claim: defendable/refutable (refute function) defendable: Alice provides decision tree and Bob cannot find a bug. refutable: Bob provides decision tree and Alice finds a bug. To make the game more interesting: defendable claims are treated first If defendable, can it be strengthened? 3/17/2018 SCG(HSR) 46
Play Game in class (abbreviated rules) Role Alice (1 -3 students from class) Role Bob (the rest of class) Role Nina (3 students from class) Alice chooses two claims: HSR(9, 2)<=3, HSR(11, 2)<=4 that she thinks she can refute. • Now play! • • Intro SCG 47
Who is the winner? • Nina keeps score. • Initially Alice and Bob have 10 points. Intro SCG 48
Bob has the following claims • • HSR(4, 1)<=4 HSR(9, 2)<=3 HSR(8, 3)<=3 HSR(4, 2)<=2 HSR(11, 2)<=4 HSR(12, 2)<=4 3/17/2018 Alice makes a decision for each claim: defendable/refutable (refute function) defendable: Alice provides decision tree and Bob cannot find a bug. refutable: Bob provides decision tree and Alice finds a bug. To make the game more interesting: defendable claims are treated first SCG(HSR) 49
Focus on • HSR(11, 2)<=4 – Alice provides decision tree. • HSR(12, 2)<=4 3/17/2018 SCG(HSR) 50
x Highest Safe Rung Decision Tree HSR(9, 2)=5 3 no yes y 1 z 6 u 0 2 highest safe rung 4 9 1 2 3 5 7 Bob, Nina check: refutation by Bob successful. Alice loses. Alice: 2 points, Bob 10 points 4 5 8 6 How could Alice have won? 3/17/2018 9 7 SCG(HSR) 8 51
HSR(11, 2)<=4 Magic for now 4 7 1 5 9 2 6 3 8 10 0 1 2 3 4 5 6 7 8 9 10
Principle of Algorithm Design • Instead of focusing on what changes from level to level, focus on what stays the same. • Find the invariant.
Initial Project Description • http: //www. ccs. neu. edu/home/lieber/courses /se-courses/cs 5500/sp 11/projects/problemstatement. html 3/17/2018 SCG(HSR) 54
Outline • Introduction – Popper Science, Renaissance History: Tartaglia and Fior • Definition of SCG – Example (Highest safe rung) • • • Applications: Teaching, Software Development, Research Claims with secrets and other protocol variants Output of SCG, Equilibrium Advantages and Disadvantages Conclusions Intro SCG 55
Applications: Software Development • Teaching Constructive Domains Intro SCG 56
Gamification of Software Development etc. • Want reliable software to solve a computational problem? Design a game where the winning team will create the software you Doesn’t Top. Coder already do this? want. • Want to teach a STEM domain? Design a game where the winning students demonstrate superior domain knowledge. STEM = Science, Technology, Engineering, and Mathematics Intro SCG 57
SCG and Top. Coder • SCG is an abstraction and generalization of what Top. Coder does. Intro SCG 58
The Traditional Approach Team A Solver A Team B Solver B Team C Solver C Parameterized by the domain. Static Benchmark HSR(9, 2)=4 HSR(25, 2)=7 Ranking measure how close to minimum Software: Solving HSR Problem: construct decision tree of min. depth Intro SCG 60
The Bio-Inspired Approach Dynamic Benchmark Solver A Team A prop-opp A Solver B Team B prop-opp B Solver C Team C Avatar A Avatar B Virtual World (Game) Ranking Avatar C prop-opp C Parameterized by the domain. Intro SCG 61
A Virtual World Avatar’s View Avatar Claims, Problems, Solutions Opponents’ communication, Feedback Administrator • Problems: Benchmark output • Solutions: Software output • Claims: statements about algorithms Results Intro SCG 62
What Scholars think about! • If I propose claim C, what is the probability that – C is successfully refuted – C is successfully strengthened • If I try to refute claim C, what is the probability that I will fail. • If I try to strengthen claim C, what is the probability that I will fail? Intro SCG 63
SCG = Scientific Community Game • Make software development more scientific. • Software developers build reputation – propose and defend claims about their software – oppose claims made by others • refute claims • strengthen claims • claim includes refutation protocol Intro SCG 64
Why a web application with avatars? Fair Evaluation. 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 avatar who has the strongest, not successfully opposed claims (like in a real scientific community). Intro SCG 65
our focus What is SCG(X) avatar Bob Alice degree of automation used by scholar 1 0 no automation human plays some automation human plays full automation avatar plays transfer to reliable, efficient software more applications: test constructive knowledge Intro SCG 66
Real Scholars and Avatars: Same rules • Are encouraged to 1. propose claims that are not easily strengthened. 2. offer claims that they can successfully support. 3. strengthen others’ claims, if possible. 4. stay active and propose new strong claims or oppose others’ claims. 5. become famous! Intro SCG 67
Clear Feedback Sense of Progress What we want Authenticity (Facebook) • 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. Possibility of Success Intro SCG 68
SCG • Gamification of software development for computational problems • A Sociotechnical System for knowledge dissemination, innovation, and integration Intro SCG 69
Software Engineering Properties fostered by SCG • Reliable (otherwise the avatar is removed from the game) • Flexible, modular (otherwise the avatar cannot be easily updated between tournaments) Adaptive and Aspect-Oriented Software is relevant! • Efficient (otherwise you cannot defend your claims and oppose the claims of others) Intro SCG 71
State of SCG-Avatar: Our Vision • Companies come to SCG website and define a competition by defining a claim domain X. • Participating teams get baby avatars generated from X that participate in daily competitions. • Competition generates a wealth of information: educated employees, good (undefeated) software, good algorithms, good potential employees. Reward is paid to the winner. Intro SCG 72
State of SCG-Avatar: Our Vision • Not only companies but faculty members who want to give their students a rich learning experience for computational problem X. • Or editors of special issues in journals who want to use a competition to get a real world comparison of all approaches to solve computational problem X. Intro SCG 73
Life of an avatar: (propose+ oppose+ provide* solve*)* Avatars propose and oppose proposed claims egoistic Alice CA 1 CA 2 CA 3 egoistic Bob social welfare opposes (1) CB 1 CB 2 provides problem (2) CA 4 LOSES solves problem WINS! not as well as she expected based on CA 2 (3) reputation 1000 transfer 200 Intro SCG 74
What is SCG(X)? Team Alice I am the best Teams Design Problem Solver Develop Software Deliver Avatar Alice Team Bob No!! Avatar Bob Let’s play constructively Administrator SCG police Intro SCG 75
competitive / collaborative Avatar Alice: claim C loses reputation r wins knowledge k Avatar Bob: opposes C, refutes: provides evidence for !C wins reputation r makes public knowledge k Intro SCG 76
Outline • Introduction – Popper Science, Renaissance History: Tartaglia and Fior • Definition of SCG – Example (Highest safe rung) • • • Applications: Teaching, Software Development, Research Claims with secrets and other protocol variants Output of SCG, Equilibrium Advantages and Disadvantages Conclusions Intro SCG 77
Protocol Variants • secrets: approximation problems • involving trusted third party – renaissance: exchange of problems Intro SCG 78
Example: Triple HSR Highest Safe Rung • Alice claims ({(25, 2, 0), (25, 2, 1), (25, 2, 2), (25, 2, 3), … , (25, 2, 25)}, 9/25, 5 seconds) • Refutation Protocol: – Bob refutes (25, 2, 17) – Alice solves problems (25, 2, *) by providing decision tree to trusted third party which reveals path p from root to 17. – predicate: p is valid and length(p) <= 9 Intro SCG 79
Protocol Variation Secrets • problem has public and private part, private part is a secret solution • predicate has secret as argument Intro SCG 80
Protocol Variation Secret Program for SCG-Avatar • problem has public and private part, private part is a secret solution and goes to administrator • Alice gives her algorithm to administrator who applies it to public part of problem • predicate has secret as argument Intro SCG 81
Example Claims involving secrets • My algorithm can solve more problems using resources r than your algorithm using r. • If I create problems for you for which I have a solution, you cannot recreate or approximate the solution with quality q using resources r. Intro SCG 82
Output and Equilibrium • Rich tournament history • What is an equilibrium in SCG? Intro SCG 83
Soundness Theorem • SCG is sound: The avatar with the best algorithms / knowledge wins (there is no way to cheat) – best: within the group of participating avatars – issues: • Does an avatar win because she is good at solving? Or good at proposing, opposing and providing? Answer: proposing, opposing and providing all reduce to solving. Intro SCG 84
SCG Equilibrium • reputations of scholars are stable • the ranking of the scholars is invariant from tournament to tournament • the science does not progress; bugs are not fixed, no new ideas are introduced • extreme example: All scholars are perfect: they propose optimal claims C(ps, q) that can neither be strengthened nor refuted. Intro SCG 85
![second-order environment! Survival in SCG(X) • [Scientific Innovation in X] Avatars get skills programmed](https://present5.com/presentation/1d2ef6421dc2764617c33faeecd2e22c/image-84.jpg "second-order environment! Survival in SCG(X) • [Scientific Innovation in X] Avatars get skills programmed")
second-order environment! Survival in SCG(X) • [Scientific Innovation in X] Avatars get skills programmed into them by clever scientists in domain X. Scientists use data mining to learn from competitions and manually improve the avatars. • [Machine Learning Innovation in X] Avatars get skills programmed into them by an avatar caregiver programmed with learning skills and data mining skills for domain X. Avatar gets updated automatically. Intro SCG 86
Blame assignment • Where is the proposer to blame? – Bad claim that is refuted. – Bug in problem finding algorithm? – Bug in problem solving algorithm? Intro SCG 87
How to use SCG(X) • Company AB needs new ideas about how to solve optimization problems in domain X. • Define claims language for X – X-problems – claims, includes protocol • Submit claims language definition to SCG server. Intro SCG 88
How to use SCG(X) • Offer prize money for winner with conditions, e. g. , performance must be at least 10% higher as performance of avatar XY that AB 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 company AB. • The SCG server sends them the basic avatar and the administrator for testing. Intro SCG 89
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 AB. Intro SCG 90
Benefits for company AB 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-refuted claims give hints about new Xspecific knowledge. • A well-tested solver for X-problems that integrates the current algorithmic knowledge in field X. Intro SCG 91
Outline • Introduction – Popper Science, Renaissance History: Tartaglia and Fior • Definition of SCG – Example (Highest safe rung) • • • Applications: Teaching, Software Development, Research Claims with secrets and other protocol variants Output of SCG, Equilibrium Advantages and Disadvantages Conclusions Intro SCG 92
Benefits/Disadvantages • Benefits – competitive / collaborative – structured feedback, game history – Teaching – Research – Software Development • Dynamic testing and evaluation • Disadvantages – addictive Intro SCG 93
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 is possible – watching over the admin Intro SCG 94
How to compensate for 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. Intro SCG 95
Benefits of SCG • Social Welfare – Supported knowledge • Claims are refuted and strengthened. • Better supported knowledge comes from better algorithms and software. Intro SCG 96
Advantage: Democratic • 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. Intro SCG 97
Experience with MAX-CSP • MAX-CSP Problem Decompositions • T-Ball (one relation), Softball (several relations, one implication tree), Baseball (several relations). • ALL, SECRET Intro SCG 98
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 Intro SCG 99
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 Intro SCG 100
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 – … Intro SCG 101
References • Karl Popper, Conjectures and Refutations, London: Routledge (1963). • Scardamalia, M. , & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3(3), 265 -283. • Renaissance: Tartaglia and Fior challenge (1535). Intro SCG 102
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. – Avatars point humans to what needs attention in problem solution / software. Intro SCG 103
Conclusions • Broad applicability, e. g. , • SCG(X) provides a software process for developing software for computational problems. • Benefits – Social Engineering: makes it fun through game. – Fair: Only hard work makes you win. – Engage a large community on one domain X. Intro SCG 104
end Intro SCG 105
State of Avatar SCG • Domain is hard-wired to Constraint Satisfaction Problems • One Master student worked on making it generic but work is not complete. Intro SCG 106
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