Multi-Agent Systems Lecture 3 University “Politehnica” of Bucarest 2005 -2006 Adina Magda Florea adina@cs. pub. ro 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: Ø coordinate their actions and behavior, a property of a MAS performing some activity in a shared environment Ø attempt to change state of the other agents Ø 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(x 0)/dist(x, x 0) or V(x)=V(x 0)/dist(x, x 0)2 S - stimulus S Agent A (stimulus triggers behavior P) x 0 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 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 q External Interface Group - interaction between KBS - KQML q Interlingua - common language of KB - KIF q 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. 11
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 chip 1) (length chip 1)) (* (width chip 2) (length chip 2))) §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 It comprises a vocabulary, a domain theory and a conceptual schemata to describe organization and interpretation § Lexicalized ontologies (Word. Net, Euro. Word. Net, Balkan. Net, Frame. Net, Mikro. Kosmos). § Ontologies for knowledge representation Person Empl IBM_E 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. , Rule. ML, 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 Schema (XMLS) • 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 § § § RDF stands for Resource Description Framework It is a W 3 C candidate recommendation (http: //www. w 3. 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, sub. Class. Of, sub. Property. Of • 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 has. Child 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 is. Part. Of is a transitive property, that has. Part is the inverse of is. Part. Of 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, RDFS § Easy to understand use • Should be based on familiar KR idioms § Formally specified § Of “adequate” expressive power § Possible to provide automated reasoning support
OWL - Web Ontology Language § W 3 C § 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: prior. Version rdf: resource="http: //www. w 3. org/TR/2003/CR-owlguide-20030818/wine"/> <owl: imports rdf: resource="http: //www. w 3. org/TR/2003/PR-owl-guide 20031215/food"/> <rdfs: label>Wine Ontology</rdfs: label> … Simple Named Classes Class, rdfs: sub. Class. Of <owl: Class rdf: ID="Winery"/> <owl: Class rdf: ID="Region"/> <owl: Class rdf: ID="Consumable. Thing"/>
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 o Assertives, which inform: the door is shut o Directives, which request: shut the door, can pelicans fly? o Commissives, which promise something: I will shut the door o Permissive, which gives permission for an act: you may shut the door o Prohibitives, which ban some act: do not shut the door o Declaratives, which causes events: I name you king of Ruritania o 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 27
§ § § 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 block 1 : language KIF : ontology Block. World : 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(P 1, P 2, . . . ) B stream-all(P) A B tell(P 1) tell(P 2) eos 29
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) 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) Instances of action Proc(A, M) Send. Msg(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 o Goal of FIPA = make available specifications that maximize interoperability across agent-based systems o 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: Ø 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 Ø 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
Ø Ø 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 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: Ø agent management support for mobility (identifying the relationship between this work and MASIF is explicitly targeted) Ø an ontology service, supporting - translation of terms between different ontologies - downloading meanings of terms, axioms, and relationships between terms - querying for relationships between ontologies Ø uploading and updating of ontologies Ø 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_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 Agent 1 : receiver Agent 2 : content (price good 2 150) : in-reply-to round-1 : reply-with bid 03 : language S 1 : 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 box 7 (loc 10 15)))) : protocol fipa-request : language fipa-sl : reply-with order 56 ) (agree : sender (agent-identifier : name j) : receiver (set (agent-identifer : name i) : content ((action (agent-identifier : name j) (deliver box 7 (loc 10 15))) (priority order 56 low)) : protocol fipa-request : language fipa-sl : in-reply-to order 56 ) 40
FIPA - Semantics SL (Semantic Language) - a quantified, multi-modal logic, with modal operators Allows to represent: qbeliefs quncertain beliefs qdesires qintentions 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 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 (Bif. B Uif. B ) Post: BB 42
KQML and FIPA ACL The two ACLs are essentially the same q Although FIPA ACL requires agents to have a limited knowledge of SL, both the ACLs do not have fixed semantics. q The FIPA ACL does not provide for facilitator agents. q 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. q KQML provides for brokering and recommendation service, whereas FIPA ACL don't really take this into account. 43
4. 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) propose. S(P) B: A<<refuse(do P) accept. R(P) B: A<<result(do P) B: A<<fail(do P) counter. R(P) Winograd, Flores, 1986 reject. R(P) accept. S(P) counter. S(P) reject. S(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], {Root. Form(W, N), is_noun(N)} S s(Conv, P, S, R, in. R, Rw, IO, Content), {member(P, [advertise, ask-if]} s(Conv, ask-if, S, R, in. R, Rw, IO, Content) [ask-if, S, R, in. R, Rw, IO, Content] | [ask-if, S, R, in. R, Rw, IO, Content], {OI is inv(IO)}, r(Conv, ask-if, S, R, _, Rw, OI, Content) r(Conv, ask-if, R, S, _, in. R, IO, Content) [tell, S, R, in. R, Rw, IO, Content] | problem(Conv, R, S, in. R, _, 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) AR 1 Refuse do(P) Accept/request do(P) Success AR 2 B is willing to do(P) Fail to do(P) BR Completed(P) Impossible to do(P) Notification of end(P) FA 1 Failure FA 2 Satisfaction FB 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. § FIPA 97. "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. w 3. 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. w 3. org/2001/sw/Web. Ont/ § http: //www. cs. man. ac. uk/~horrocks/Slides/index. html 49
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