WP7: Empirical StudiesPresenters: Paolo Besana, Nardine Osman, Dave Robertson

Outline of This Talk

• Introduce overall framework

• Identify four key areas:– Interaction availability– Consistency interaction-peer– Consistency peer-peer– Consistency with environment

In each of these areas it is impossible to guarantee the general property we ideally would require, so the goal of analysis is to identify viable engineering compromises and explore how they scale.

Basic Conceptual Framework

P M(P,R)

P1

Pn

EP

EP1

EPn

P = process nameR = role of PM(P,R) = Interaction model for P in role REP = environment of P

Simulation as Clause Rewriting

Ensuring Interactions are Available

P M(P,R)

P1

Pn

EP

EP1

EPn

MP

RR(P) → ◊(M(P,R)M(P) (i(M(P,R)) → ◊a(M(P,R))))

R(P) = Roles P wants to undertakeMP = Interactions known to P {M(P,R) , …}i(M(P,R)) = M(P,R) is initiateda(M(P,R)))) = M(P,R) is completed successfully

Specific Question

• Suppose that the same interaction patterns are being used repeatedly in overlapping peer groups.

• To what extent can basic statistical information about success/failure of interaction models solve matchmaking problems?

See Deliverable 7.1 for discussion of this

Consistency Peer - Interaction Model

P M(P,R)

P1

Pn

EP

EP1

EPn

K(P) K(M(P,R))

AK(P) (BK(M(P,R)) ◊BK(M(P,R))) → (A B)

K(X) = Knowledge derivable from X(F) = F is consistent

Specific Question

• Each interaction model imposes temporal constraints

• Peers have deontic constraints

• What sorts of properties required by peers (e.g. trust properties) or by interaction modellers (e.g. fairness properties) can we test using this information alone.

ExampleIn an auction, the auctioneer agent wants an

interaction protocol that enforces truth telling

on the bidders’ side.

A = [bid(bidder,V)⇒win(bidder,PV)] ⋀ [bid(bidder,B)⇒win(bidder,PB) ⋀ B≠V] ⋀ PB≮PV

where A∈K(P)

We would like to verify:

A∈K(P) ∧(B∈K(M(P,R))∨◊B∈K(M(P,R))) →σ(A∧B)

1

2

3

4

M(P,R)

Verifying σ(A∧B)

Verify M(P,R) satisfies A:

Is A satisfied at state 1?

If result is achieved,

then terminate else, go to next state(s)

and repeat

1

2

3

4

M(P,R)

1

2

3

4

M(P,R)

1

2…

…

Property Checking Framework

interactionstate-space

temporalproperties

deonticconstraints

Mo

del

Ch

ecke

rX

SB

sys

tem

TablePrologengine

Temporal Proof Rules

LCC Transition Rules

satisfies(E,tt) true

satisfies(E,Φ1⋀Φ2) satisfies(E,Φ1) satisfies(E,⋀ Φ2)

satisfies(E,Φ1⋁Φ2) satisfies(E,Φ1) satisfies(E,⋁ Φ2)

satisfies(E,<A>Φ) ∃ F. trans(E,A,F) ⋀ satisfies(F,Φ)

satisfies(E,[A]Φ) ∀F. trans(E,A,F) ⋀ satisfies(F,Φ)

satisfies(E,μZ.Φ) satisfies(E,Φ)

satisfies(E,νZ.Φ) dual(Φ,Φ’) ⋀ ¬satisfies(E,Φ’)

Temporal Proof Rules

trans(E::D,A,F) trans(D,A,F)

trans(E1 or E2,A,F) trans(E1,A,F) trans(E⋁ 2,A,F)

trans(E1 then E2,A,E2) trans(E1,A,nil)

trans(E1 then E2,A,F then E2) trans(E1,A,F) ⋀ F ≠ nil

trans(E1 par E2,A,F par E2) trans(E1,A,F)

trans(E1 par E2,A,E1 par F) trans(E2,A,F)

trans(M⇐P,in(M),null) true

trans(M⇒P,out(M),null) true

trans(E←C,#(X),E) X in C sat(X) sat(C) ⋀ ⋀

trans(E←C,A,F) (A ≠ #) sat(C) trans(E,A,F)⋀ ⋀

LCC Transition Rules

Consistency Peer - Peer

P M(P,R)

P1

Pn

EP

EP1

EPn

K(P) K(P1)

AK(P) PiP(M(P,R)) BK(Pi) → (A B)

P(M(P,R)) = Peers involved in M(P,R)

Specific Question

• Agents in open environments may have different ontologies

• Guaranteeing complete mappings between them is infeasible (ontologies can be inconsistent, can cover different domains, etc)

• Agents are interested in performing tasks: mapping is required only for the terms contextual to the interactions

• Repetition of tasks provides the basis for modelling statistically the contexts of the interactions

• To what extent can interaction models can be used to focus the ontology mapping to the relevant sections of the ontology?

Approach

• Predicting the possible content of a message before processing can help to focus the mapping:– With no knowledge of the context and of the state of an

interaction, a received message can be anything– the context can be used to guess the possible content of

messages, filtering out unrelated elements– the guessed content is suggested to the ontology mapping

engine

• The entities in a received message mi(e1,...,en) are bound by the context of the interaction:– some entities are specific to the interaction type (purchase,

request of information,...),– the set of possible entities is bound by concepts previously

introduced in the interaction,– different entities may appear in a specific message with different

frequencies

Implementation

• Creating the model:– Entities appearing in messages are counted, obtaining their prior

and conditional frequencies – Ontological relations between entities in different messages are

checked and the verified relations are counted

• Predicting the content of a message:– When a message is received, the probability distribution for all the

terms is computed using the collected information and the current state of the interaction

– The most probable terms form the set of suggestions for the ontology mapping engine

Two phases:

The aim is to obtain the smallest possible set that is most likely to contain the entities actually used in the message.

Mapping Evaluation Framework

Testing

• Interactions are abstract protocols, and agents have generated ontologies– allows us to simulate different types of relations between the

messages

• Community preferences over elements (best sellers, etc) are simulated by probability distributions

• Interactions are run automatically hundreds of times• Results are compared with a uniform distribution of the

entities (simulates no knowledge about context)– Equivalent size for same success rate – Equivalent success rate for same size of suggestion set

Provisional Results

• After 100 interactions, the predictor is able to provide a set smaller than 7% of the ontology size containing, 70% of the time, the term actually used in message m2

• If all terms are equiprobable, the probability is directly proportional to the size of the (randomly picked) set, as shown above.

Consistency Peer - Environment

P M(P,R)

P1

Pn

EP

EP1

EPn

K(EP) K(P)

AK(P) BK(EP) → (A B)

Specific Question

• Suppose we have a complex environment with adversorial agents

• For specific goals, how complex do interaction models need to be in order to raise group performance significantly?

Environment Simulation Framework

Groupconvergence random coordinated

Comparativeperformance

Environmentsimulator

Simulated agents

Interaction model

Coordinating peer

a(hunter,Id):: sawHimAt(Location) => a(hunter,RID) visiblePlayer(Location) and strafeAttempt(Location,Location) or strafeAttempt(Location,Location) sawHimAt(Location) <= a(hunter,RID) or movementAttempt(random_play)

You can be a hunter if you send a message revealing the location of a visible opponent player upon whom you are making a strafing attack or make a strafing attack on a location if you have been told a player is there or otherwise just do what seems right