ebm and abm (the slie approach)
DESCRIPTION
EBM and ABM (The SLIE approach). Carles Sierra Mike Wooldridge. ABM vs EBM. Behaviours. Equations. Individuals. Observables. Proposal for Design Rules. EBM. Analysis. ABM. Analysis. Design Rules. Desired results. If EBM:Price stable and ABM:Price increases then - PowerPoint PPT PresentationTRANSCRIPT
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EBM and ABM(The SLIE approach)
Carles SierraMike Wooldridge
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ABM vs EBM
Individuals Observables
Behaviours Equations
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Proposal for Design Rules
EBM
ABM
Analysis
Analysis
Design Rules
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Desired results
If EBM:Price stable and ABM:Price increases then Increase average beta for customers
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Methodology
P1: Social interaction analysis(manual)
P2: Individual behaviouranalysis
(semi-automatic)
P3: Experiment design(manual)
Step 1: EBM Model
Step 2: Electronic Institutionmodel
Step 3: Agent specifications
Step 4: Multiagent system
P5: Model checking(automatic)
P4: Experimetanalysis (Semi-
automatic)
P6: ExperimentAnalysis(manual)
P7: ExperimentAnalysis (manual)
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What is an Electronic Institution
Role. Standardised patterns of behaviour required of all agents playing part in a given functional relationship.
Dialogic Framework. Ontological elements and communication language (ACL) employed during an agent interaction.
Scene. Agents meetings whose interaction is shaped by a well-defined protocol.
Performative Structure. Complex activities specified as connections among scenes.
Normative rules. Defines the consequences of the agent actions within scenes.
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Scenes
Scene is a pattern of multi-agent conversation.
During the execution new agents can join the
scene or some of the participants can leave
the scene at definite states depending on their
role.
An scene can be multiple-instantiated and
played by different groups of agents.
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Scenes(II)
Scene is specified by a finite state oriented graph where the nodes represent the different states and oriented arcs are labelled with the events that will make the conversation state evolve.
For each role is defined a set of access and
exit states.
Minimum and maximum number of agents per
role.
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Performative Structure
Complex activities can be specified by establishing
relationships among scenes that: capture causal dependency. define synchronisation mechanisms. establish parallelism mechanisms. define choice points that allow roles leaving
a scene to choose which activity to engage in next.
establish the role flow policy.
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EIM: Supply chain
Customer
trader
X:customer,Y:supplier
X:trader,Y:trader
X:customer,Y:supplier*
X:customer,*Y:supplier
X:trader,Y:trader
X:customer,Y:supplier
X:trader,Y:trader
X:customer,Y:supplier
X:customer,Y:supplier
X:trader,Y:trader
external
agora-keeper*
external
external accountant*Root
Negotiation
Agora
OutputDelivery
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EIM: ISLANDER Language
(define-scene-type negotiationtNOU as roles = (supplier customer) dialogical_framework = Negotiation states = (w1 w2 w3 w4 w5 w6) initial_state = w1 final_states = (w5 w6) acces_states = ((supplier (w1)) (customer (w1))) exit_states = ((supplier (w5 w6)) (customer (w5 w6))) agents_per_role = ((1 leq supplier leq 1) (1 leq customer leq 1)) connections = ((w1 w2 (offer ?s:supplier ?c:customer (Sign ?d:deal ?date) ?date)) (w1 w3 (offer ?c:customer ?s:supplier (Sign ?d:deal ?date) ?date)) ((w2 w3 w4) w2 (offer !s:supplier !c:customer (Sign ?d:deal ?date) ?date)) ((w2 w3 w4) w3 (offer !c:customer !s:supplier (Sign ?d:deal ?date) ?date)) (w2 w4 (reject !c:customer !s:supplier (Sign !d !date))) (w3 w4 (reject !s:supplier !c:customer (Sign !d !date))) (w2 w5 (accept !c:customer !s:supplier (Sign !d !date))) (w3 w5 (accept !s:supplier !c:customer (Sign !d !date))) ((w2 w4 w3) w6 (withdraw !c:customer !s:supplier)) ((w2 w4 w6) w6 (withdraw !s:supplier !c:customer))) constraints =())
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Software components
Developmentcomplete To be implemented Under development
JADE+PROLOG(Laboratory)
Promela(Model checker)
EBM(SIMILE)
SIMILE(simulator andgraphic editor)
EIM(ISLANDER-)
MABLE(simulator)
Multiagentsystem
(PROLOG)
AM(Promela)
ISLANDER(verifier and
ghaphic editor)
AM(MABLE)
AM(PROLOG)
Experimentresults
MILORD(editor andsimulator)
Designrules
(MILORD)
EIM(ISLANDER)
AGEN TGENERATOR
PROMELATRANSLATOR
ModelCheckingResults
MASSynthesizer
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Model Checking Multi-Agent Systems with MABLE
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Model Checking
Technique for verifying finite state systems
To verify that S satisfies property P: generate system state space -- a model which
encodes all computations of system express P as formula of temporal logic determine whether formula is valid in this
model if “yes”, then system satisfies specification
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State Space Explosion
Systems containing interacting components generate astronomically large state space
Main problem in model checking: managing this state space explosion
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CTL Model Checking
Uses CTL branching temporal logicLow complexity (polynomial time) in
size of model & specificationSymbolic CTL model checking with
OBDDs main technique for handling state space explosion
Implemented in e.g., SMV, nuSMV
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LTL Model Checking
LTL model checking more expensive (PSPACE complete)
But techniques to manage complexity: partial order reduction “on the fly” verification
Implemented in SPIN (AT&T Bell Labs)Used for verification of
software/protocols
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SPIN Operation
L T L S p e c ifica tion
S P IN
S im u la tion run
"ye s ", c la im is va lid "n o " -- h e re is co un te r e xam p le
E xe cu ta b le ve rif ie r
C C o m p ile r
P ro to co l A N a lyse r (P A N )(C P ro g ra m )
P R O M E L A S yste m D esc rip tion
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MABLE
Language & tool for automatic formal verification of multi-agent systems
MABLE system contains agents & claims
Makes use of SPIN model checker for simulation automatic verification
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Agents in MABLE
Programmed using imperative language iteration, sequence, selection a la C data types: int, bool + enums, records synchronisation via locks
Agents have beliefs, desires, intentions may be nested
Communication via “inform”, “request” with FIPA-like semantics
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Claims in MABLE
MABLE systems are augmented by claims
Claims expressed using MORA: BDI modalities (believe i p)...
LTL modalities <>p, []p, p U q
limited quantification forall a : agent ...
pseudo dynamic logic (does I stmt)
macro definitions #define MBEL(i,j,p)...
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The MABLE Compiler
Translates MABLE agents into PROMELA
Three components to translation: imperative constructs to PROMELA
(easy) BDI components to PROMELA data
structures claims to SPIN-format LTL formulae
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Translating Claims
Works by: expanding out quantification (finite
domains) translating modalities to statements
about data structures renaming predicates with propositions annotating PROMELA model with new
propositions for (does I stmt) construct
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MABLE Operation
L T L S p e c ifica tion
S P INT yp e tit le h e re
S im u la tion run
E xe cu ta b le ve rif ie r
C C o m p ile r
P ro to co l A N a lyse r (P A N )(C P ro g ra m )
P R O M E L A S yste m D esc rip tion
M A B L E C o m p ile r
M A B L E P rog ra m + M O R A C la im s
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Example MABLE Code
// this is a claim
claim <>(believe agent1 (intend agent2 (believe agent2 a==10)))
// this is an agent
agent agent2 {
await (intend agent1 (believe agent2 a == 10));
if (intend agent1 (believe agent2 a==10)) && (a != 10)) print(“Agent 1 is a LIAR!”); else print(“Agent 1 tells the TRUTH!”); }