1. SCG layers or SCG stages Karl and Yue

2. Layers of Constraints We can look at the process of game design as a successive layering of constraints on a game. Every new rule you add, every resource you define, is just one more constraint on the players. At the start of the design process you may have nothing, and the players could do anything at all; by the end, the player experience is sharply defined and heavily constrained in a way that is fun. (We will address what “fun” actually is, later in the course.) http://gamedesignconcepts.wordpress.com/2009/07/0 9/level-4-the-early-stages-of-the-design-process/

3. Connections Software design Executable code How to structure the code Architecture Several software designers with different responsibilities Game design Game to play How to structure game def Game architecture Several game designers with different goals

4. SCG Architecture Three layer architecture – Meta game designer-level fixed by SCG designer: Karl Lieberherr defines structure of playground: – domain (instance, solution, …), claim(setOfInstances, protocol, …) defines basic game mechanism: propose (oppose|agree) constrains playground-level – Playground designer-level fixed by SCG user freezes language definitions for Domain (Instance, Solution) Protocol, etc. constrains scholar-level – Scholar-level game is now defined and scholars define their game strategies providing methods like propose, refute, strengthen, …

5. Levels of Definition: Domain Meta game designer-level – Instance – Solution – valid Playground designer-level – Instance language definition PL – Solution language definition SL – valid, quality = specific code Scholar-level – A specific instance in PL (sentence expressed in PL) – A specific solution in SL – constrains scholar (executing code)

6. Levels of Definition: Claim Meta game designer-level – setOfInstances – protocol: defines protocol language PRL Playground designer-level – setOfInstances, intensional definition predicate = specific code – defines specific protocol using PRL Scholar-level – constrains scholar to define instance (execution) – defines interaction with other scholars

7. Four layer SCG architecture There are many ways to model a scientific community. For example, – one can argue that a scholar should also define a confidence level for a claim. Confidence influences reputation gain and loss. – a scholar should pass a simple entrance exam before participating in a competition We need four layers – meta-meta, meta, playground, scholar

8. Contributors to Claim Definition Claim: setOfInstances, q, r, protocol – meta-meta designer: Karl Lieberherr – specific claim: CNF-pos(2,0.7,2*|P|) Claim: setOfInstances, q, r, protocol, confidence – meta-meta designer: Ahmed Abdelmeged – specific claim: CNF-pos(2,0.7,2*|P|, 0.99) meta-meta: requires confidence, paramterized reputation update formula, meta: sets parameter, playground: adds nothing scholar: specific confidence meta-meta: requires protocol in some protocol language, meta: protocol language grammar, playground: specific sentence for grammar defines CNF-pos, scholar: uses CNF-pos

9. Contributors to Claim Definition Claim: setOfInstances, q, r, protocol – meta-meta designer: Karl Lieberherr – specific claim: CNF-pos(2,0.7,2*|P|) Claim: setOfInstances, q, r, protocol, confidence – meta-meta designer: Ahmed Abdelmeged – specific claim: CNF-pos(2,0.7,2*|P|, 0.99) meta-meta: requires confidence info, meta: defines parameterized formula, playground: defines parameter values, scholar: specific confidence meta-meta: requires protocol in some protocol language, meta: protocol language grammar, playground: specific sentence for grammar defines CNF-pos, scholar: refers to CNF-pos

10. Contributors to Claim Definition specific claim: CNF-pos(2,0.7,2*|P|) meta-meta: overall shape: 4 components – setOfInstances, q, r, protocolName meta: defines how to specify a set of instances through a language. – number k: all clauses are of length k playground: defines k to be in a range: [2,17] scholar: chooses one k

11. SCG as Board Game? Pieces: Claim, Instance, Solution Board: – Claim: proposed, refuted, defended, agreed – Instance: provided, solved

12. Domain-specific languages for games ERIC WALKINGSHAW and MARTIN ERWIG (2009). A domain-specific language for experimental game theory. Journal of Functional Programming, 19, pp 645-661 doi:10.1017/S0956796809990220 http://en.wikipedia.org/wiki/Domain- specific_language

13. Evolution through stages meta-meta – Domain = Instance Solution ValidPredicate QualityFunction. – Claim = Set(Instance) Quality Resource.

14. Evolution through stages meta – Domain = Instance Solution ValidPredicate QualityFunction. – Claim = Set(Instance) Quality Resource. – Instance = Integer InstanceId … – Solution = InstanceId …

15. Evolution through stages meta – Domain = Instance Solution ValidPredicate QualityFunction. – Claim = Set(Instance) Quality Resource. – Instance = Integer InstanceId … – Solution = InstanceId … – ValidPredicate = Instance Solution ValidBody. – QualityFunction = Quality Instance Solution QualityBody. – ValidBody = JavaCode.

16. Evolution through stages playground – Domain = Instance Solution ValidPredicate QualityFunction. – Claim = Instances Quality Resource. – Instance = Integer InstanceId InstanceStructure. – Solution = InstanceId SolutionStructure. – ValidPredicate = Instance Solution ValidBody. – QualityFunction = Quality Instance Solution QualityBody. – ValidBody = JavaCode. – InstanceStructure = List(Constraint). – Constraint = [ Weight ] Relation List(Variable). – Weight = Integer. – Relation = Integer. – Variable = Ident. – SolutionStructure = List(Literal). – Literal = Pos | Neg Variable. – Pos =. – Neg = “!”. – Instances = Set(Relation). // all CSPs using fixed set of relations – relations must be in 0..255 – ValidBody, QualityBody defined in Domain sentence

17. Evolution through stages scholar – claim CSP-pos({22},0.375,2*|P|) CSP-neg({22},0.45, 2*|P|) – instance 22(a b c) 22(a b d) 22(a c d) 22(b c d) – solution a !b !c !d

18. Meaning of resource in negative claim Alice claims that she can find a instance p that Bob cannot solve with the given resource.