Southern Illinois University,Carbondale, IL - 62901
{rahmad, rahimi, bidyut}@cs.siu.edu
Abstract In this paper we propose an agent modelinglanguage named CAML that provides a comprehensiveframeworkfor representing all relevant aspects ofa multi-agent system: specially, its configuration and the reasoningabilities of its constituent agents. The configurationmodeling aspect of the language supports natural groupingand mobility, and the reasoning framework is inspired byan extension of the popular BDI theory of modelingcognitive skills ofagents. We present the motivation behindthe development of the language, its syntax, and aninformal semantics.
1. INTRODUCTION
Multi-Agent Systems have appeared as a crucial andexciting field in computer science in the last couple ofdecades. A relevant and popular definition of an agent isgiven by Wooldridge [17]: "An agent is a computer systemsituated in some environment and that is capable offlexible, autonomous action and communication with otheragents in this environment in order to meet its designobjectives." An agent is often specified by its autonomousnature, communication capabilities, its location in anenvironment, and its ability to react to and affect change inits environment. A multi-agent system then is a recognizedcollection of such agents that coexist and interact in anenvironment where the overall control and information isgenerally distributed (decentralized). Multi-agent systemsfind application in a wide range of domains including:business and finance including e-commerce, human-computer interactions, information management, trafficcontrol, social sciences, computer games, and severalInternet-based applications such as search agents.
The ad-hoc nature of development, analysis andverification of today's multi-agent systems has resulted inlargely unreliable products for the end-user. Formalmethods are almost a necessity when it comes todeveloping systems in critical scenarios such as military,medical systems, and air traffic control [1, 2]. Although
they represent a significant investment in time andexpertise, the advantages are significant: formalspecification of a system by itself can be useful in terms ofgetting the system requirements right by eliminating errorsat the design phase; verification of a system can allowdesigners to ascertain the correctness of a system design - ifit is behaving properly, in a correct fashion, and if it isfunctioning according to a set of requirements; furtheranalysis of a formally specified system can providesignificant insights into its internals such as itsperformance; and with proper and mature tools available,the formal specification can be used directly or indirectlyas a guide to the final implementation of the system.However, current formal methods do not provide featuressuitable for representing a comprehensive view of multi-agent systems. Either they are geared towards representingits evolving configuration and structure, or they are usedprimarily to reason about the behavior of the agents. Boththese aspects are crucial when dealing with multi-agentsystems, and cannot be effectively studied in isolation ofthe other.
In this paper we present a formal modeling language calledCAML that will fill this void. CAML is a process algebrathat is inspired partly from -calculus based formalismsand also the popular Belief-Desire-Intention theory ofmodeling the cognitive skills of agents to represent theirbehavior. The result is a formal framework that providesthe proper primitives and constructs to model and analyze atypical multi-agent system.
2. CURRENT STATE OF THE ART
The last couple of decades have seen a growing interest inthe development of formal foundations for multi-agentsystems. Due to the complexity, non-determinism, andvariety of applications, no single theory or framework hasclaimed prominence, with the possible exception of BDI.Not only that, a number of "formal" tools cannot really betermed as such, in the stricter meaning of the term. At leastthe following conditions should hold: the formalism shouldhave a system specification language with multi-agentsystem related constructs for abstractions such as agents,agent communication, reasoning, system hierarchy, and
Figure 1. Structural representation of a multi-agent system and its expression in API-Calculus
agent mobility; it should have a complete set ofoperational semantics; and these semantics should allowfor verification either through system refinement orautomatic model checking. Several formalisms that arecurrently in use in this field do not meet theserequirements. These can be classified as: specificationlanguages originally meant for software engineering, suchas VDM [7], ETL [3], and tools and languages builtaround diagrammatic system specification, includingUML-related tools such as AML [4], and PASSI [5].
The major approaches towards formal modeling andanalysis can be divided into two broad classes in terms ofthe particular aspects of multi-agent system they are gearedtowards:
a) Formalisms for modeling structural organization: multi-agent system tend to have an incredibly complex structurebecause of a number of reasons: the number of agents, thecreation and destruction of agents, the evolving nature ofthe communication structure, possible mobility of agents,and issues of coordination and cooperation between agents,among other complexities. Traditionally, system structurehas been studied best using formalisms which supportconcurrent computation such as Petri Nets, Actor model,and process calculi. Process calculi that extend pi-calculus[10], itself an extension of CCS [11], have been particularlypopular for describing systems with concurrent computingelements. These calculi treat the communication of namesas the basic primitive and pi-calculus itself is known to beTuring complete [12]. Although this class of formalmethods is suitable in modeling aspects of concurrentcomputation, to model the reasoning, behavior, andplanning, inherent in all multi-agent system, would bedifficult if not totally impossible, due to the unavailabilityof any high-level constructs for this purpose. Figure 1
shows a typical example of how formalisms of this classrepresent multi-agent systems.
b) Formalisms for modeling behavioral aspects: Possiblythe more exciting part of multi-agent system research is theanalysis of agent behavior, reasoning, and planning.Indeed, this is where the intelligence of individual agentscome into play - decades of artificial intelligence researchprovide a solid foundation here. The formal methods thathave gained prominence in this area have their foundationin logic, specifically modal logic such as temporal andepistemic, that model agent reasoning as a set of itscognitive skills. This includes the seminal work done byRao and Georgoff [13] on formalisms for the Belief-Desire-Intention (BDI) model. BDI has since then formedthe core of several research undertakings for reasoningabout agent behavior in both formal and practical contexts[14]. Figure 2 shows a typical representation of a multi-agent system by formalisms of this class.
What's Missing
Both classes of formalisms discussed above suffer fromseveral shortcomings that make it hard to adopt any of theformal methods available today as a comprehensive multi-agent system modeling tool. The two classes represent asignificant divide in objectives that has not been bridged.The structural organization and behavior of a multi-agentsystem are both integral to the understanding and analysisas well as the implementation process. Class a) has asignificant drawback in itself: although pi-calculus and itsextensions have enjoyed considerable success forspecifying and reasoning about concurrent systems, thecomplexity of multi-agent systems requires a much moresophisticated framework than what they offer, includinghigh-level abstractions such as those for agents andcommunication. There have been some efforts in this
capability for searching the departments selling a certain productmessage=search(?pr);condition=null;do{send(?AgI: EMarket needProduct(?pr)) }effects=null;
foundProduct {capability for adding to the knowledge base the names of the interesting domains
Figure 2. Behavioral representation of an multi-agent system and its expression in CLAIM
direction, most significantly Ambient Calculus [16] and itsextensions, and API-Calculus. For class b), the BDI modelof agent behavior and planning has been recognized as avery suitable foundation. However, none of the BDI basedformalisms or specification languages deal with theconfiguration and structure of multi-agent systems.
Two formal methods have specific relevance to our work:the first is psi-calculus [9], a process algebra that intends toformalize, in an abstract manner, the plan execution modelof agent computation that is common to several BDI-basedframeworks such as PRS [6] and dMARS [8]. However, itdoes not deal with the configuration and composition of theagent system as a whole. Another process calculus thatmakes a significant attempt to provide a comprehensiveformalism is CLAIM [CLAIM]. CLAIM meets the need oftackling both the configuration and reasoning. Specifically,the aspects of hierarchical composition of agents, agentmobility, and agent communication are handled in depth.However, the model of agent reasoning is not mature -agent execution is carried out by message communication,and initiation of agent methods. Although this kind of amodel helps in translating a formal model of a system intoan executable implementation, it does away with the morerealistic and adaptable reasoning models of BDI and itsextensions.
In our opinion, the unavailability of a single formal methodwhich can be used for both the structural and behavioralanalysis is a crucial aspect that needs to be addressed inmulti-agent system research. At least part of the problem isthe complexity of such an undertaking. Besides the task ofcomposing the right syntax for such a formalism (whichshould deal with the sticky problems of what an agent anda multi-agent system is), the major work will be in workingout the operational semantics. The latter can be extremelycomplex if the operational semantics of the much simplercalculi such as API-Calculus and Ambient Calculus are anyindication. Also, any formal method that hopes to be
adapted in the field of multi-agent system, should provide aconcrete methodology for verification of the systems thatcan be described using it. These may include basictechniques such as bisimulation or more advanced ones likemodel-checking.
3. A COMPREHENSIVE INTELLIGENT AGENTMODELING CALCULUS
In Fig. 3, a comprehensive view of a multi-agent system ispresented. It is obvious that such a view will be closer tothe reality of implemented systems than the ones in lastsection - it recognizes the necessity of representing notonly an agent's behavior (its intelligence/reasoning) butalso how that agent is situated in its environment in termsof its hierarchy, mobility and communication. Although thechoice of primitives can always be debated upon, theintention of developing a formal language that is able toallow the representation of all relevant and characteristicfeatures of multi-agent system has not been shared by otherformalisms in the past. Of course, the involvement of sucha variety of constructs makes the calculus much morecomplicated than the ones we have described in the lastsection, but we believe that the availability of the calculuswill be of tremendous benefit in the near future, speciallyas a foundation for more high-level tools that are easier tointeract with. This includes the development of a high-level language that uses the operational semantics of theformal language and provides a much more usable syntaxand programming ability, and also a visual developmentenvironment that will allow defining and composingCAML based agents. Also, the availability of a robust andcomprehensive formal language like CAML will serve tofoster a more constructive dialog between researchers whoare involved in the different aspects of multi-agent systemwith a more holistic outlook. In practice, it will alsoadvance the development of more robust and easilyanalyzable multi-agent system-based applications.
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proactive behavior of an agent.
Knowledge, K. The knowledge construct is a repository forinformation that reflects an agents view or belief of itsenvironment including other agents. K is an abstractrepresentation of the agent's belief state and does notprovide the composition of individual belief items. Thechange in agent's knowledge in the progress of a multi-agent system's execution is handled by knowledgetransitions. These transitions form the core of defining theagent behavior and are described in detail later.
Process Calculiview of MAS
Agent Programming Languageview of MAS
Knowledge)
Quu
( Plans
,--,Pi tio
Figure 3. A comprehensive view of representing Multi-Agent Systems
The proposed formalism, named Complete Agent ModelingLanguage (CAML), is a process algebra that promises a
formal framework in which a comprehensive model ofmulti-agent systems may be represented, including itsorganization and the reasoning ability of its constituentagents. The syntax and semantics of CAML are not basedon any other formalisms (which is true for some otherlanguages such as API-calculus, -calculus or SEAL).With the tradeoff of more work in the development ofsemantics, it allowed for much more freedom andflexibility. For modeling the organizational aspects of a
system, CAML provides single-level grouping primitivesnamed milieus. A milieu can constrict agents in a boundarywhich defines a specific execution entity as well as restrictsthe visibility of an agent to those in its parent milieu. Formodeling the execution and reasoning abilities the relevantprimitives are knowledge, events, actions, conditions, eventqueues, intention queues, and actions - constructs that are
inspired by the BDI model of cognitive skills and planexecution, also adopted in agent-oriented programminglanguages such as AgentSpeak [15]. For the sake of brevity,we only present the syntax and an informal semantics ofCAML in this paper; a more detailed treatment of thesyntax and a formal operational semantics based on
reduction rules such as those defined for API-Calculus willfollow in a future paper.
4. SYNTAX AND SEMANTICS
In this section, we present the syntax of CAML and an
informal semantics that will give the reader an
understanding of the mechanics of the language.
As detailed later, an agent is composed of its knowledge,Conditions, Capabilities, Event Queue, Plan, and IntentionQueue. These basic primitives make up the reactive and
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Events, E. An event can be external (initiated by anotheragent message, change in environment) or internal (changein an agent's knowledge). These events initiate theexecution chain of agents by triggering actions under theguard of contexts. Again, an event is an abstracted entityand does not represent any internal composition.
Conditions, C. A condition acts as a guard for triggering an
action for an agent. Each condition acts as a post-conditionthat needs to be satisfied in order for an action or actions totake place for the occurrence of an event. Every agentkeeps a set of conditions which hold true in a particularstate.
Actions, Act. An action represents the tasks an agent can
perform in the future. Actions are modeled as processes inthe tradition of process calculi based on -calculus. Anaction in CAML can be:
* T, an internal action
* TK, internal knowledge transition
* join(m), agent joins milieu m
* leave, agent leaves its parent milieu
* send(e), agent sends event e as a broadcast
T, the internal action represents any action that is notexplicitly defined such as those required for internalhousekeeping. T7 is an explicit knowledge transition of theagent, instead of those initiated by the an external event.The join (m) and leave actions represent the mobility of theagent and instruct the agent to join milieu m or leave itsparent milieu. The action send(e) broadcasts the event e toits environment and replaces the low-level send and receiveactions of -calculus based algebras. The action is notdirected towards a particular agent in the environment,rather it is supposed to represent an event that takes placein multi-agent system to which any agent can responddepending on its own capabilities. This also makes a
receive action unnecessary. The significance of theseactions will be apparent in the next sub-section when we
describe the semantics. Actions can be composed by the thefollowing operators: ., and + which result in sequential,parallel and non-deterministic execution.
Knowledge Transition, Knowledge or an agent's beliefsystem plays a crucial role in its behavior. The transitionsbetween the knowledge states represents the changing
Mil:
beliefs of the agent either due to internal actions and eventsor external events. A transition is written as:
elelaki I kz2
where the transition from knowledge state k1 to k2 isqualified by the occurrence of event e, under the conditionc, and results in the action a. Either of the three qualifierscan be null or compound constructs. For example, atransition with a null action implies that the event is onlymeant to bring about the transition in knowledge of theagent and no actual execution needs to carried out;similarly, a transition with a null condition implies onewithout any restriction. Although, it is named as such, thetransition does not have to actually result in a change in theagent's knowledge, i.e., k1 can be equal to k2 in which casethe purpose for the event is the action alone.
Capabilities, Q. The capabilities of an agent represent thepossible transitions that can occur in that agent. Thereforethe capabilities is a set of transitions
Event Queue, Eq. Any event that needs to be handled by anagent is acknowledged by adding it to the event queue.
Intention Queue, Iq. This serves as a temporally task buffer.Any action that an agent intends to take is added to itsintention queue.
Plan, P. A plan is what drives an agent's executionaccording to what it needs to achieve in order to satisfy itsgoal. In this sense and how it is implemented in thesemantics, it can be seen as a cross between plan and desireof the classical BDI model. Specifically it is an eventless
clatransition ki c+ k2 where the transition takes place bythe satisfaction of the condition alone.
Agent, A. An agent is a composition of some of theconstructs defined above. Specifically, an agent is definedas:
A-=[K,Q,C,P,EQ,IQ]
Therefore, an agent can be identified by specifying itsknowledge, its capabilities, the conditions that hold true,the set of plans that will let the agent achieve its goals, andits intention and event queues. An agent changes its statesthroughout its lifetime as a result of the transitions of itsconstituents. Therefore, the state space of the fourconstructs reflects the agent configuration in CAML.
Milieu, M. A milieu acts as a grouping container that alsoconstricts its agents in terms of execution andcommunication. Inspired from API-Calculus [18], it willprovide an isolated computational unit where agents canreside and execute. Agents can join and leave a milieu, andthe communication with agents external to the milieu isrestricted. Milieus provide the basic support for mobility as
well, and will be able to model different real-world entitiessuch as a single computer, a network, an execution-sandbox, etc. It can be both, an abstract idea (a milieu ofagents that are responsible for a very specific task), as wellas a physical reality (agents present in the local network). Itis devoid of any capabilities to perform actions, and otheragent-specific tendencies and in that sense it is alsodifferent from the idea of an environment, which issupposed to act as more than a container like a milieu does.
An Informal Semantics of CAML
A complete and formal operational semantics is notprovided in this paper for the sake of brevity and clarity. Itincludes basic reduction rules in the style of -calculus thatdescribe the execution of agents as guided by events andplans, and also communication and configuration aspects ofthe multi-agent system such as mobility across milieus. Inthe following, we describe a more informal semantics ofthe language with the aim of giving the reader an idea ofthe intention behind the choice of the primitives, how thecomposition of these primitives constructs a multi-agentsystem, and the mechanics and dynamics of agentexecution and configuration.
Agent Execution. The core of agent execution revolvesaround the knowledge transition. As mentioned earlier, the
elelatransition k1 + k2 is the result of an external event ewhich in the presence of condition c, will lead to theknowledge transition of k1 to k2 itself and also the action a.An external event, when it occurs, is added to the agent'sevent queue. The selection of an event from the queuedepends on the selection policy of the particular agent, andcan be non-deterministic, ordered temporally or by apriority value assigned to the events either externally orinternally. After the selection of an event, it is matchedsyntactically with the agent's capabilities. When a match isfound with a particular transition's triggering event, thetransition is then said to be initialized. At this point, thecondition c is matched with the conditions in C and if thecondition holds true, then the knowledge transition takesplace and the action a is added to the intention queue. Theintention queue works under the same selection principle asthe event queue, and tasks (actions) are selected forexecution.
The event based execution of tasks described above issolely for external events triggered by other agents or bythe change in the system configuration. Tasks triggeredinternally are guided by the agent's plans. At every iterationof system progress, each plan of an agent is checked forpossible execution: if the eventless-transition's condition issatisfied, the knowledge transition takes place along withthe addition of the corresponding action to the intentionqueue. As for the actions triggered by external events,actions in the event queue corresponding to plan executioncan include the send(event) action, which is responsible forinter-agent communication.
Agent mobility. Agent mobility is handled by the two actionprimitives, join and leave, which of course can be activated
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by either an event or plan execution. This allows an agentto move around milieus and gives it different contexts forexecution.
5. CONCLUSION AND FUTURE WORK
We have presented a new formal language for specificationof multi-agent systems. The stress is on providing acomprehensive and holistic outlook towards viewing agents- their composition, execution, and mobility. The syntaxprovides provides all the basic primitives for the generalrepresentation of a typical multi-agent system. The BDI-inspired behavioral representation is able to model both theproactive and reactive nature of an autonomous, intelligentagent, while mobility and organization is handled bycomposing agents in milieus.
We intend to present a complete operationalsemantics in an extended paper in the future. A majormotivation behind developing CAML has been toeventually provide a mechanism for verification of multi-agent systems. This would require a robust operationalsemantics, including type-checking that will enforceconsistency in system composition, and equivalencerelations of both structural congruence and bisimulationvarieties. In the long run, CAML is intended to form afoundation for a high-level programming language and alsoa visual development environment that will allow thedesign, analysis and implementation of multi-agent systemsin a comprehensive and easy to use framework.
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