Transcript Communication

Multi-Agent Systems
Lecture 3
University “Politehnica” of Bucarest
2005-2006
Adina Magda Florea
[email protected]
http://turing.cs.pub.ro/blia_06
Agent communication
Lecture outline



The nature of communication
Indirect communication
Direct communication

•
•
•
•
•

Agent Communication Languages
Content languages
Ontologies
Theory of speech acts
KQML
FIPA and FIPA-ACL
Interaction protocols
1. The nature of communication
1.1 Human communication
 Communication is the intentional exchange of information brought
about by the production and perception of signs drawn from a shared
system of conventional signs (AIMA, Russell&Norvig)  language
 Action (communicative act); intentional stance
1.2 Component steps of communication
Speaker
Hearer
 Intention
 Generation
 Synthesis
Syntax
 Perception
Semantics
 Analysis
Pragmatics
 Disambiguation
 Incorporation
3
1.3 Artificial Communication
 low-level language vs high-level languages
 direct communication vs. indirect communication
Agent communication/ MAS communication






low-level communication: simple signals, traces, low-level languages
high-level communication - cognitive agents, mostly seen as intentional
systems
Communication in MAS
Implies interaction
The environment provides a computational infrastructure where
interactions among agents take place.
The infrastructure includes protocols for agents to communicate and
protocols for agents to interact
4
Communication protocols = enables agents to
exchange and understand messages
Interaction protocols = enable agents to have
conversations, i.e., structured exchanges of
messages
Aim  Communication enables agents to:
 coordinate their actions and behavior, a property of
a MAS performing some activity in a shared
environment
 attempt to change state of the other agents
 attempt to make the other agents perform some
actions
5
2. Indirect communication
2.1 Signal propagation - Manta, A. Drogoul 1993
 An agent sends a signal, which is broadcast into the environment, and
whose intensity decreases as the distance decreases
 At a point x, the signal may have one of the following intensities
V(x)=V(x0)/dist(x,x0) or V(x)=V(x0)/dist(x,x0)2
S - stimulus
S
Agent A

(stimulus triggers
behavior P)
x0


Agent B
(stimulus triggers
behavior P)
Reactive agents
2.2 Trails - L. Steels, 1995
 agents drop "radioactive crumbs" making trails
 an agent following a trail makes the trail faint until it disappears
6
2.3 Blackboard systems, Barbara Hayes-Roth, 1985
 Blackboard = a common area (shared memory) in which agents can





exchange information, data, knowledge
Agents initiates communication by writing info on the blackboard
Agents are looking for new info, they may filter it
Agents must register with a central site to receive an access
authorization to the blackboard
Blackboard = a distributed knowledge computation paradigm
Agents = Knowledge sources (KS)
Control
KS
Blackboard
KSAR
KS
Cognitive agents
KS
KS
7
3. Direct communication
Sending messages
 method invocation – Actors
 exchange of partial plans – coordination of cooperative
agents
ACL = Agent Communication Languages
 Need to communicate knowledge  knowledge
representation
 Need to understand the message in a context 
ontologies
 Communication is seen as an action - communicative
acts
8
3.1 Agent Communication Languages
Concepts (distinguish ACLs from RPC, RMI or
CORBA, ORB):
 An ACL message describes a desired state in a declarative





language, rather than a procedure or method invocation
ACLs handle propositions, rules, and actions instead of
objects with no associated semantics - KR
ACLs are mainly based on BDI theories: BDI agents
attempt to communicate their BDI states or attempt to alter
interlocutor's BDI state – Cognitive Agents
ACLs are based on Speech Act Theory – Communicative
Acts
ACLs refer to shared Ontologies
Agent behavior and strategy drive communication and lead
to conversations Protocols
9
Origins of ACLs
Knowledge Sharing Effort - DARPA, 1990
 External Interface Group - interaction between KBS - KQML
 Interlingua - common language of KB - KIF
 Shared, Reusable Knowledge Bases - Ontolingua
 Communication primitives
and protocols
 Content languages
•
•
•
•
•
KIF
Prolog
Clips
SQL
FIPA-SL, FIPA-KIF
ACL
Content language
Ontology
 Ontologies
 DAML
 OWL
DARPA Agent Markup Language (August 2000).
The goal of the DAML effort is to develop a language
and tools to facilitate the concept of the Semantic Web.
10
3.2 Content languages for ACL
(Knowledge representation)
 Description Logics (DL) - a formalism for expressing concepts and
their interrelationships.
In DL, concepts are organized into IS-A hierarchies.
Concepts are specifications such that given an individual
(object instance) a DL system can recognize the individual and
determine which concepts it belongs to.
DL systems also perform subsumption checking.

Knowledge Interchange Format (KIF) - is based on first order
predicate logic and has a LISP-like prefix syntax.
KIF is capable of expressing facts and rules.
KIF provides constructs for describing procedures, i.e.
programs to (possibly) be executed by an agent.
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Knowledge Interchange Format (KIF)
 Facts
(salary 015-46-3946 john 72000)
(salary 026-40-9152 michael 36000)
(salary 415-32-4707 sam 42000)
 Asserted relation
(> (* (width chip1) (length chip1))
(* (width chip2) (length chip2)))
Rule
(=> (and (real-number ?x)
(even-number ?n))
(> (expt ?x ?n) 0))
Procedure
(progn (fresh-line t)
(print "Hello!")
(fresh-line t))
12
3.3 Ontologies
Ontology = a specification of objects,
concepts, and relationships in a
particular domain
Person
Empl
IBM_E
It comprises a vocabulary, a domain theory
and a conceptual schemata to describe
organization and interpretation
 Lexicalized ontologies (WordNet,
EuroWordNet, BalkanNet, FrameNet,
MikroKosmos).
 Ontologies for knowledge representation
Stud
Pupil
Sun_E
Joe
Alice
Person
Man
Empl
Joe
Woman
Stud
Alice
13
Ontology Languages

Wide variety of languages for “Explicit Specification”
• Graphical notations
– Semantic networks
Ontology Languages

Wide variety of languages for “Explicit Specification”
• Graphical notations
– Topic Maps
Ontology Languages

Wide variety of languages for “Explicit Specification”
• Graphical notations
– UML
Ontology Languages

Wide variety of languages for “Explicit Specification”
• Graphical notations
– RDF
Ontology Languages

Wide variety of languages for “Explicit
Specification”
• Logic based
– Description Logics (e.g., OIL, DAML+OIL, OWL)
– Rules (e.g., RuleML, LP/Prolog)
– First Order Logic (e.g., KIF)
Many languages use “object oriented” model
based on:

Objects/Instances/Individuals
• Elements of the domain of discourse
• Equivalent to constants in FOL

Types/Classes/Concepts
• Sets of objects sharing certain characteristics
• Equivalent to unary predicates in FOL

Relations/Properties/Roles
• Sets of pairs (tuples) of objects
• Equivalent to binary predicates in FOL

Such languages are/can be:
•
•
•
•
Well understood
Formally specified
(Relatively) easy to use
Amenable to machine processing
Web “Schema” Languages

Existing Web languages extended to facilitate content
description
• XML  XML Schema (XMLS)
• RDF  RDF Schema (RDFS)

XMLS not an ontology language
• Changes format of DTDs (document schemas) to be XML
• Adds an extensible type hierarchy
– Integers, Strings, etc.
– Can define sub-types, e.g., positive integers

RDFS is recognisable as an ontology language
• Classes and properties
• Sub/super-classes (and properties)
• Range and domain (of properties)
RDF and RDFS


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RDF stands for Resource Description Framework
It is a W3C candidate recommendation
(http://www.w3.org/RDF)
RDF is graphical formalism ( + XML syntax +
semantics)
• for representing metadata
• for describing the semantics of information in a
machine- accessible way

RDFS extends RDF with “schema vocabulary”,
e.g.:
• Class, Property
• type, subClassOf, subPropertyOf
• range, domain
Problems with RDFS

RDFS too weak to describe resources in sufficient
detail
• No localised range and domain constraints
– Can’t say that the range of hasChild is person when applied to
persons and elephant when applied to elephants
• No existence/cardinality constraints
– Can’t say that all instances of person have a mother that is also a
person, or that persons have exactly 2 parents
• No transitive, inverse or symmetrical properties
– Can’t say that isPartOf is a transitive property, that hasPart is the
inverse of isPartOf or that touches is symmetrical
• …

Difficult to provide reasoning support
• No “native” reasoners for non-standard semantics
• May be possible to reason via axiomatisation
Web Ontology Language
Requirements
Desirable features identified for Web Ontology
Language:
Extends existing Web standards
• Such as XML, RDF, RDFS

Easy to understand and use
• Should be based on familiar KR idioms

Formally specified

Of “adequate” expressive power

Possible to provide automated reasoning support
OWL - Web Ontology Language
 W3C
 OWL - designed for use by applications that need to process
the content of information instead of just presenting
information to humans.
 OWL facilitates greater machine interpretability of Web
content than that supported by XML, RDF, and RDF Schema
(RDF-S) by providing additional vocabulary along with a
formal semantics.
 OWL has three increasingly-expressive sublanguages:
 OWL Lite supports a classification hierarchy and simple constraints.
 OWL DL supports maximum expressiveness while retaining
computational completeness (all conclusions are guaranteed to be
computed) and decidability (all computations will finish in finite time).
 OWL Full supports maximum expressiveness and the syntactic
freedom of RDF with no computational guarantees.
OWL - Web Ontology Language
Ontology header
<owl:Ontology rdf:about="">
<rdfs:comment>An example OWL ontology</rdfs:comment>
<owl:priorVersion rdf:resource="http://www.w3.org/TR/2003/CR-owlguide-20030818/wine"/>
<owl:imports rdf:resource="http://www.w3.org/TR/2003/PR-owl-guide20031215/food"/>
<rdfs:label>Wine Ontology</rdfs:label>
…
Simple Named Classes
Class, rdfs:subClassOf
<owl:Class rdf:ID="Winery"/>
<owl:Class rdf:ID="Region"/>
<owl:Class rdf:ID="ConsumableThing"/>
3.4 Theory of Speech Acts
J. Austin - How to do things with words, 1962, J. Searle - Speech acts, 1969
A speech act has 3 aspects:
 locution = physical utterance by the speaker
 illocution = the intended meaning of the utterance by the speaker (performative)
 prelocution = the action that results from the locution
Alice told Tom: "Would you please close the door"
locution
illocution
content
prelocution: door closed (hopefully!)
Illocutionary aspect - several categories
 Assertives, which inform: the door is shut
 Directives, which request: shut the door, can pelicans fly?
 Commissives, which promise something: I will shut the door
 Permissive, which gives permission for an act: you may shut the door
 Prohibitives, which ban some act: do not shut the door
 Declaratives, which causes events: I name you king of Ruritania
 Expressives, which express emotions and evaluations: I am happy
26
3.5 KQML - Knowledge Query and Manipulation
Language
A high-level, message-oriented communication language and
protocol for information exchange, independent of content syntax
(KIF, SQL, Prolog,…) and application ontology
KQML separates:
 semantics of the communication protocol (domain independent)
 semantics of the message (domain dependent)
3 (conceptual) layers
Core of KQML
Describes low level
communication
parameters:
- identity of sender and
receiver
- an unique id associated
with the communication
Content
Communication
Message
- identity of the network
protocol with which to deliver
the message
- speech act or performative
Optional
- content language
- ontology
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
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Syntax
S-expressions used in LISP
KQML performatives are classified:
Queries - These performatives are used to send questions for evaluation
somewhere.
Generative - Used for controlling and initiating the exchange of messages.
Response - Used by a agent in order reply to queries.
Informational - Informational performatives are used to transfer information.
Capability definition - Allows an agent to learn about the capabilities of other
agents and to announce its own to the agent community.
Networking - Networking performatives make it possible to pass directives to
underlying communication layers.
Example
(ask-one :sender joe
:receiver ibm-stock
:reply-with ibm-stock
:language PROLOG
:ontology NYSE-TICKS
:content (price ibm ?price) )
(tell :sender willie
:receiver joe
:reply-with block1
:language KIF
:ontology BlockWorld
:content (AND (Block A)(Block B)
(On A B)) ) 28
1. Query performatives:
ask-one, ask-all, ask-if, stream-all,...
(stream-all
:sender willie
:receiver ibm-stock
:content (price ?VL ?price ) )
A
ask-one(P)
B
ask-all(P)
A
tell(P)
tell(P1,P2,...)
B
stream-all(P)
A
B
tell(P1)
tell(P2)
eos
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2. Generative performatives:
standby, ready, next, rest, discard, generate,...
3. Response performatives:
reply, sorry ...
insert(P)
A
4. Generic informational performatives:
tell, untell, insert, delete, ...
5. Capability performatives:
advartise, subscribe, recommend...
delete(P)
B
tell(P)
A
untell(P)
B
6. Network performatives:
register, unregister, forward, route, ...
Facilitator
In fact, KQML contains only 2 types of illocutionary acts:
assertives and directives
+ facilitator and network-related performatives (no
necessarily speech acts)
30
Facilitator agent





= an agent that performs various useful communication services:
maintaining a registry of service names (Agent Name Server)
point-to-point
forwarding messages to named services
routing messages based on content
ask(P)
matchmaking between information providers and clients
providing mediation and translation services
B
A
tell(P)
subscribe(ask(P))
A
tell(P)
tell(P)
B
recommend(ask(P))
advertise(ask(P))
reply(B)
recruit(ask(P))
advertise(ask(P))
reply(A)
A
B
tell(P)
A
ask(P)
B
tell(P)
31
Semantics of KQML (Labrou & Finin)
 Use preconditions and postconditions that govern the use of a performative + the
final state for the successful performance of the performative
 Uses propositional attitudes: belief, knowledge, desire, intentions
Preconditions: the necessary states for an agent to send a performative and
for the receiver to accept it and successfully process it; if the precondition
does not hold, the most likely response is error or sorry
Postconditions - describe the state of the sender after successful utterance of
a performative and of the receiver after the receipt and processing of a
message
Completion condition - the final state after a conversation has taken place
and that the intention associated with the performative that started the
conversation has been fulfilled
Propositional attitudes
Bel(A,P)
Know(A,S)
Want(A,S)
Int(A,S)
Instances of action
Proc(A,M)
SendMsg(A,B,M)
32
tell(A,B,X)
A states to B that A believes the content X to be true, Bel(A,X)
Pre(A): Bel(A,X)  Know(A, Want(B, Know(B, Bel(A,X))))
Pre(B): Int(B, Know(B, Bel(A,X)))
or Bel(A,X)
Post(A): Know(A, Know(B, Bel(A,X)))
no unsolicited information
Post(B): Know(B, Bel(A,X))
Completion: Know(B, Bel(A,X))
advertise(A,B,M)
A states to B that A can and will process the message M from B, if it receives one
Int(A, Proc(A,M))
commisive act
Pre(A): Int(Proc(A,M))
Pre(B): NONE
Post(A): Know(A, Know(B, Int(A, Proc(A,M)))
Post(B): Know(B, Int(A, Proc(A,M)))
Completion: Know(B, Int(A, Proc(A,M)))
33
3.6 FIPA and FIPA - ACL
Foundation for Intelligent Physical Agents, 1996

Goal of FIPA = make available specifications that maximize
interoperability across agent-based systems

FIPA Committees: ACL, agent specification, agent-software
interaction
 As KQML, FIPA ACL is based on speech act theory; it sees
messages as communication acts (CA); syntax similar to KQML
 Differs in: the names of CAs, set of CAs, and semantics
34
FIPA standard define normative specifications for:

agent management (or agent platform services)
 white pages via an Agent Name Server (ANS)
 yellow pages via a Directory Facilitator (DF)
 registration in a given DF defines a domain (agent community)
 an Agent Platform defines a logical "place" containing an ANS,
DF, management tools, and a collection of agents
 interplatform communication takes place via an Agent
Communication Channel (ACC), which defaults to CORBA IIOP

agent communication language - FIPA ACL
 based on speech acts
 has a formal semantics
 also included are several predefined protocols (e.g., contract-net
negotiation and auction protocols), and the concept of applicationspecific protocols
35


agent-software integration
 defines Agent Request Broker and Wrapper roles
 allows an agent system to integrate non-agent software
 details of how wrapper communicates with wrapped
software are left to the implementor (a Wrapper is an
agent, and communicates with other agents via ACL)
several reference applications for:
 personal travel assistance
 personal assistant
 network provisioning and management
 audio/video entertainment and broadcasting
36
FIPA has extended these specifications, including work on:
 agent management support for mobility (identifying the relationship
between this work and MASIF is explicitly targeted)
 an ontology service, supporting
- translation of terms between different ontologies
- downloading meanings of terms, axioms, and relationships
between terms
- querying for relationships between ontologies
 uploading and updating of ontologies
 additional applications, e.g., product design and manufacturing
agents
FIPA does not currently constrain the low-level implementation of
agents to any great extent, nor, except for defining agent platform
services, does it constrain the infrastructure a great deal.
37
FIPA - ACL
FIPA communicative acts
Informatives
- query_if, subscribe, inform, inform_if, confirm,
disconfirm, not_understood
Task distribution
- request, request_whenever, cancel, agree, refuse, failure
Negotiation
- cfp, propose, accept_proposal, reject_proposal
38
 FIPA-SL
(inform
:sender Agent1
:receiver Agent2
:content (price good2 150)
:in-reply-to round-1
: reply-with bid03
: language S1
:ontology hp-auction
:reply-by 10
:protocol offer
:conversation-id conv-1 )
39
 FIPA-SL
(request :sender (agent-identifier :name i)
:receiver (set (agent-identifer :name j)
:content ((action (agent-identifier :name j)
(deliver box7 (loc 10 15))))
:protocol fipa-request
:language fipa-sl
:reply-with order56 )
(agree
:sender (agent-identifier :name j)
:receiver (set (agent-identifer :name i)
:content ((action (agent-identifier :name j)
(deliver box7 (loc 10 15))) (priority order56 low))
:protocol fipa-request
:language fipa-sl
:in-reply-to order56 )
40
FIPA - Semantics
SL (Semantic Language) - a quantified, multi-modal logic, with
modal operators
Allows to represent:
beliefs
uncertain beliefs
desires
intentions
B  - belief D  - desire U  - uncertain belief
PG  - intention
Bif  - express whether an agent has a definite opinion one way
or another about the truth or falsity of 
Uif  - the agent is uncertain about 
41
FIPA - Semantics
The semantics of a CA is specified as a set of SL's formulae that
describe:
 Feasibility preconditions - the necessary conditions for the sender the sender is not obliged to perform the CA
 Rational effect - the effect that an agent can expect to occur as a
result of performing the action; it also typically specifies conditions
that should hold true of the recipient
The receiving agent is not required to ensure that the expected effect
comes about
The sender can not assume that the rational effect will necessary
follow
<A, inform(B, )>
Pre: BA   BA (BifB   UifB )
Post: BB 
42
KQML and FIPA ACL
The two ACLs are essentially the same
 Although FIPA ACL requires agents to have a limited knowledge
of SL, both the ACLs do not have fixed semantics.
 The FIPA ACL does not provide for facilitator agents.
 This is a major drawback as using facilitator is one of the best
way to overcome different systems using different content
language and providing matchmaking service.
 KQML provides for brokering and recommendation service,
whereas FIPA ACL don't really take this into account.
43
4. Interaction protocols
Interaction protocols = enable agents to have
conversations, i.e., structured exchanges of
messages
 Finite
automata
 Conversations in KQML
 Petri nets
 FIPA IP standards:
• FIPA-query, FIPA-request, FIPA-contract-net, ...
4.1 Finite state automata
COOL, Barbuceanu,95
A:B<<ask(do P)
B:A<<accept(do P)
proposeS(P)
B:A<<refuse(do P)
acceptR(P)
B:A<<result(do P)
rejectR(P)
B:A<<fail(do P)
counterR(P)
counterS(P)
Winograd, Flores, 1986
acceptS(P)
rejectS(P)
45
4.2 Conversations in KQML
Use Definite Clause Grammars (DCG) formalism for the specification of
conversation policies for KQML performatives
DCGs extend Context Free Grammars in the following way:
 non-terminals may be compound terms
 the body of the rule may contain procedural attachments, written as "{" and "}"
that express extra conditions that must be satisfied for the rule to be valid
Ex: noun(N)  [W], {RootForm(W,N), is_noun(N)}
S  s(Conv, P, S, R, inR, Rw, IO, Content), {member(P, [advertise, ask-if]}
s(Conv, ask-if, S, R, inR, Rw, IO, Content) 
[ask-if, S, R, inR, Rw, IO, Content] |
[ask-if, S, R, inR, Rw, IO, Content], {OI is inv(IO)},
r(Conv, ask-if, S, R, _, Rw, OI, Content)
r(Conv, ask-if, R, S, _, inR, IO, Content) 
[tell, S, R, inR, Rw, IO, Content] |
problem(Conv, R, S, inR, _, IO)
Labrou, Finin, 1998
46
4.3 Petri nets
Ferber, 1997
Petri net = oriented graph with 2 type of nodes:places and transitions;
there are moving tokens through the net - representation of dynamic aspect of processes.
Tokens are moved from place to place, following firing rules.
A transition T is enabled if all the input places P of T posses a token (several other rules may be defined).
A marking is a distribution of tokens over places. Colored Petri-nets
A wants to do P,
A cannot do P
B does not want
to do(P)
DA
DB
Request do(P)
AR1
Refuse do(P)
Accept/request do(P)
Success AR2
B is willing
to do(P)
Fail to do(P)
BR
Completed(P)
Impossible
to do(P)
Notification of end(P)
FA1
FB
Failure
FA2
Satisfaction
47
References
 M. Huhns, L. Stephens. Multiagent systems and societies of agents. In
Multiagent Systems - A Modern Approach to Distributed Artificial Intelligence,
G. Weiss (Ed.), The MIT Press, 2001, p.79-120.
 M. Wooldrige. Reasoning about Rational Agents. The MIT Press, 2000, Chapter
7
 Y. Labrou, T. Finin. Semantics and conversations for an agent communication
language. In Readings in Agents, M. Huhns & M. Singh (Eds.), Morgan
Kaufmann, 1998, p.235-242.
 J. Ferber - Multi-Agent Systems. Addison-Wesley, 1999, Chapter 6
 T. Finnin, R. Fritzson - KQML as an agent communication language. In Proc. of
the Third International Conference on Information and Knowledge Management
(CIKM'94), ACM Press, 1994.
 M. Singh. Agent communication languages: Rethinking the principles. IEEE
Computer, Dec. 1998, p.40-47.
 Y. Labrou, T. Finnin, Y. Peng. Agent communication languages: The current
Landscape. IEEE Computer, March/April 1999, p. 45-52.
 FIPA97. "Agent Communication Language" Specification FIPA, 11/28/97
48
Web References
DARPA KSE http://www-ksl.stanford.edu/knowledge-sharing/
KQML
http://www.cs.umbc.edu/kqml/
KIF
http://logic.stanford.edu/kif/
Ontolingua http://www-ksl-svc.stanford.edu:5915/&service=frame-editor
FIPA
http://www.fipa.org/
DAML
http://www.daml.org/
OWL
http://www.w3.org/TR/owl-guide/
References for Ontologies (due to prof. Stefan Trausan)
 Constandache, G.G., Ştefan Trăuşan-Matu, Ontologia şi hermeneutica
calculatoarelor, Ed. Tehnică, 2001
 Gruber, T., What is an Ontology, http://www.kr.org/top/definitions.html
 J. Sowa, Ontologia şi reprezentarea cunoştinţelor, în (Constandache şi
Trăuşan-Matu, 2001)
 http://www.w3.org/2001/sw/WebOnt/
 http://www.cs.man.ac.uk/~horrocks/Slides/index.html
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