2 Traditional AIMuch traditional AI has been concerned with how an agent can be constructed to function intelligently, with a single locus of internal reasoning and control implemented in a Von Neumann architecture.But intelligent systems do not function in isolation --- they are at the very least a part of the environment in which they operate, and the environment typically contains other such intelligent systems.Thus, it makes sense to view such systems in societal terms.
3 Outlines What is MAS? What are the benefits of using MAS? What are the challenges of developing MAS?What are suitable domains for using MAS?
4 What is MAS?An extension of the agent technology where a group of loosely connected autonomous agents act in an environment to achieve a common goal.This is done either by cooperation where knowledge is shared among agents, or competition where knowledge is not shared.Demo:
5 What is MAS?Multiagent Systems combine autonomous entities, each having diverging interests or different information.This comprehensive overview of the field offers a computer science perspective but also draws on ideas from …Game theoryEconomicsPhilosophyLinguisticsComputer ScienceLogicOperations research
6 Benefits of Using MAS (motivations) Distributed computations are sometimes easier to understand and easier to develop, esp. when the problem being solved is itself distributed.Distribution can lead to computational algorithms that might not have been discovered with a centralized approach.There are also times when a centralized approach is impossible, because the systems and data belong to independent organizations that want to keep information private and secure for competitive reasons.The rationale for interconnecting computational agents and expert systems is toenable them to cooperate in solving problems,to share expertise,to work in parallel on common problems,to be developed and implemented modularly,to be fault tolerant through redundancy,to represent multiple viewpoints and the knowledge of multiple experts, andto be reusable
7 Challenges (1) Environment In a MAS, the action of an agent not only modifies its own environment but also that of its neighbors.This necessitates that each agent must be able to predict the actions of other agents in order to decide the optimal action that would guide it towards the optimal goal.This type of concurrent learning could result in non-stable behavior and can possibly cause chaos.The problem is further complicated if the environment is dynamic.Each agent needs to differentiate between the effects caused by the actions of other agents and variations in the environment itself.
8 Challenges (2) Perception In a distributed multi-agent systems, the agents are scatted all over the environment.Each agent has a limited sensing capability because of the range and coverage of the sensors connected to it.Therefore, decisions based on partial observations made by each of the agents could be sub-optimal and achieving a global solution by these means become intractable.
9 Challenge (3) Abstraction In an agent system, it is assumed that an agent knows its entire action space, and mapping of the state space to action space can be done by experience. However, in MAS, every agent does not experience all of the states.To create a map, it must be able to learn from the experience of other agents with similar capabilities or decision making powers. In the case of cooperating agents which have similar goals, this can be done easily by establishing communication between the agents.In the case of competing agents, it is not possible to share information as each agent tries to increase its chances of winning.It is therefore essential to quantify how much local information and how much of the capabilities of the other agents must be known in order to create an improved model of the environment.
10 Challenges (4) Conflict resolution Conflicts stem from the lack of global view available to each agentActions selected by an agent to modify a specific internal state may be bad for another agent.Under these circumstances, information on the constraints, action preferences and goal priorities of each agent must be shared with other agents to improve cooperation. However, a major problem with MAS is determining when and to which agent to communicate this information to.
11 Challenges – (5) Inference Single agent systemInference can be easily drawn by mapping the State Space to the Action Space based on trial and error methods.MASIt is difficult as the environment is being modified by multiple agents that may or may not be interacting with each other.Further, the MAS might consist of heterogeneous agents, which are agents with different goals and capabilities.
12 Characteristics of Multiagent Environments Provides an infrastructure specifying communication and interaction protocolsAre typically open and have no centralized designerContain agents that are autonomous and distributed and may be self-interested or cooperative.
13 Agent Communications Coordination Dimensions of meaning Message types Communication levelsSpeech actsKQMLKIFOntologiesOther communication protocols
14 Agent Communications Coordination Dimensions of meaning Message types Communication levelsSpeech actsKQMLKIFOntologiesOther communication protocols
15 Agent Communications Why communicate? In order to achieve better the goals of themselves or of the society/system in which they exist.Enable the agents to coordinate their actions and behavior, resulting in systems that are more coherent.
16 Agent Communications Coordination Coordination is a property of a system of agents performing some activity in a shared environment.The degree of coordination is the extent to which they avoid extraneous activity byreducing resource contention,avoiding livelock and deadlock, andmaintaining applicable safety conditions.
17 Agent Communications Coordination Different ways of coordination Cooperation: non-antagonistic agentsNegotiation: competitive or simply self-interested agentsTo coordinate successfully, each agent must maintain a model of the other agents, and also develop a model of future interactions sociability!CoordinationCooperationCompetitionPlanningNegotiationDistributed planningCentralized planning
18 Agent Communications Coordination How a multiagent can maintain global coherence without explicit global control?Coherence: how well a system behaves as a unit.How?Some form of organizationSocial commitmentEconomic principles of marketsCoherence and Optimalityare intimately relatedMarket mechanism – less effective in computing optimal allocations of resources (Simon, 1996).Organizational structures are essential for computing optimal allocations of resources.Herbert Simon. The Sciences of the Artificial. MIT Press, Cambridge, MA, third edition, 1996.
19 Agent Communications --- Speech Acts What is Speech acts?Speech act theory views human natural language as actions, such as requests, suggestions, commitments, and replies.Why do we need Speech Acts?Goal: to model communication among computational agents using human communication.To insure that there is no doubt about the type of message.Problem:in communication among humans, the intent of the message is not always easily identified.I am coldAn assertionI am coldA request for a sweaterA demand for an increase in room temperature
20 Agent Communications --- Speech Acts A speech act has three aspects:Locution ---- the physical utterance by the speakerIllocution ---- the intended meaning of the utterance by the speakerPerlocution --- the action that results from the locutionJohn, please close the windows.Intent for the message as q request or a demandPhysical sound/or text messageIf all goes well, the window being shut
21 Agent Communications --- Speech Acts Speech act theory uses the term performative to identify the illocutionary force (言外之意) of this special class of utterance:Example performative verbs: Promise, report, convince, insist, tell, request, and demandIllocutionary force can be broadly classified asassertives (statement of fact),directive (commands in a master-slave structure),commissives (commitments),declaratives (statements of fact), andexpressives (expressions of emotions)
22 Agent Communications Knowledge Query and Manipulation Language (KQML) KQML is a protocol for exchanging information and knowledge.Structure:(KQML-performative:sender <word>:receiver <word>: reply-with: in-reply-to:language <word> e.g. KIF, Prolog, LISP, SQL:ontology <word> define the common concepts, attributes, and relationshipsfor different subsets of world knowledge:content <expression>… )SyntaxLisp-like.Arguments identified by keywords preceded by a colon --- may be given in any orderThe KQML-performatives: speech act performatives.SemanticsKQML-performatives: Domain independentMessage: defined by the fieldsParameters of the message passingThe semantics of the message
23 Agent Interaction Protocols Interaction protocols govern the exchange of a series of messages among agents --- conversation.The objective of the protocols:To maximize the payoffs (utilities) of the agents --- in cases where the agents have conflicting goals or are simply self-interestedTo maintain globally coherent performance of the agents without violating autonomy, i.e., without explicit global control, in cases where the agents have similar goals or common problems, as in distributed problem solving (DPS):Determine shared goals;Determine common tasks;Avoid unnecessary conflicts;Pool knowledge and evidence.
24 Agent Interaction Protocols --- Coordination Why actions of multiple agents need to be coordinated? becausethere are dependencies between agents’ actions,there is a need to meet global constraints, andno one agent has sufficient competence, resources or information to achieve system goals.Examples of coordination:Supplying timely information to other agentsEnsuring the actions are synchronizedAvoiding redundant problem solving
25 Agent Interaction Protocols AND/OR Goal Graph The actions of agents in solving goals can be expressed a representation through a classic AND/OR goal graph.The goal graph includes a representation of the dependencies between the goals and the resources needed to solve the primitive goals (leaf nodes of the graph). Indirect dependencies can exist between goals through shared resources.