Executing Specifications of Social Reasoning Agents Iain Wallace - - PowerPoint PPT Presentation

Executing Specifications of Social Reasoning Agents

Iain Wallace Michael Rovatsos

School of Informatics The University of Edinburgh

DALT’10

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Overview

1 Why is general social reasoning interesting? 2 How does ESB specify social reasoning? 3 How is it implemented? 4 A simple example. 5 Conclusions

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Motivation

• A general, intuitive, structure for social reasoning.
• A guide for implementation without prescribing reasoning

methods.

• Bounded rationality principles for social reasoning.
• Automated analysis of designs.

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What is ESB?

• Expectation - Strategy - Behaviour
• ESB assumes (Inter)actions are governed not only by goals,

but also:

• Expected behaviour of others.
• Experience and observation.
• Potential future behaviour of self and others.

ESB is not a concrete social reasoning method.

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What’s in an Expectation?

An example from the card game Rummy: Name = Expectation1 Condition = Agent(b) picked up 2♦ Expected Φ = Agent(b) is collecting 2s Test = +(Agent(b) picks up a 2) −(Agent(b) ignores/discards a 2) Response ρ+ = remove({E 2, E 5}) add({E 3, E 4}) Response ρ− = remove({E 1})

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Reasoning with Expectations

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Introducing ESB-RS

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Expectations: Generating the FSM

For each expectation E next(E.Condition) := case: For each other expectation O If (E in O.Tp.addSet) OR (E in O.Tm.addSet) then next(E.Condition) : {True,False} If (E in O.Tp.remSet) OR (E in O.Tm.remSet) then next(E.Condition) : {DC} end for Condition = True : {True,False}; Condition = False : {True,False}; Condition = DC : {DC}; end case next(E.Test) := case: Condition = DC : {NA} true : {Tp, Tm, NA} end case end for

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Strategies and the FSM

• Strategies define the style of an agent’s reasoning, and provide

a means to bound it.

• Defined as restrictions on the strategy graph.
• Implemented as constraints on the FSM transition relation.
• An optimistic agent:

next(E.Test : {Tp, NA})

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Checking Behaviour Conditions

Behaviours are the interface between ESB and BDI: condition ⊲ action Where an “action” in this case is a belief plus BDI plan. Conditions are CTL formulae: E♦(Expectation1

Φ)

“I might hold Expectation 1 in the future”

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Capturing Simple Social Reasoning - Expectations

Expectation

• Exp. Joint Intention

Condition pwag(self,Y,A,Q) ∧ pwag(Y,self,A,[Q∧pwag(self,Y,A,Q)]) Φ JointIntention(self,Y,A) T+ A ∨ ¬Q ∨ F T− (A ∨ ¬Q ∨ F) [source(¬Y)] ρ+

• ρ−
• pwag(X,Y,A,Q) = persistent weak achievement goal, from X to Y,

to achieve A relative to condition Q.

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Capturing Simple Social Reasoning - Behaviours

Only one behaviour is needed for an agent to act on a JI: ji(self,Y,A) ⊲ (bel haveJI(A)) When to request an action: E♦(ji(self,Y,A)) ∧ (desire A) ⊲ (bel requestJI) When to agree to an action: request(A,Q,F) ∧ E♦(ji(self,Y,A) ∧ (desire A) ⊲ (bel agreeRequest(A))

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Capturing Simple Social Reasoning - An Extension

A simple team plan:

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Capturing Simple Social Reasoning - An Extension

New expectations: New Behaviours: E♦(ji(self,Y,A)) ∧ free(Y) ⊲ (bel requestJI(Y)) request(A,Q,F) ∧ E♦(ji(self,Y,A)) ∧ free(self) ⊲ (bel agreeRequest(A))

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Conclusions

• ESB-RS is an implemented general social reasoning system.
• Implemented with NuSMV model checker and Jason BDI

interpreter.

• ESB can capture simple social reasoning concepts in generic

fashion.

• But how general is ESB-RS, what classes of social reasoning

can be captured?

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