An Introduction to Agent TechnologiesPresentation OverviewWorking definition of an agentAgent ProgramAgents EnvironmentsBasic CharacteristicsBasic Characteristics (cont’d)Slide 8Agent Mental State (BDI)Example: Trading AgentSlide 11Reactive AgentRational AgentsBoundedly Rational AgentsLearning AgentSlide 16AutonomyBelief RepresentationBenefits of DeclaritivismOther Properties of AgentsAgent SocietiesSoftware InfrastructureCommunicationOntologiesSocial OrganizationEmergent BehaviorE-commerce exampleLegacy System ExamplePersonal AssistantsSlide 30Conclusion1An Introduction to Agent TechnologiesPeter Wurman, NCSUYelena Yesha, UMBCOlga Streltchenko, UMBC2Presentation OverviewWorking definition of an agentAgent characteristics and propertiesAgent societiesExamples3Working definition of an agent“Agents are active, persistent (software) components that perceive, reason, act, and communicate”Huhns and Singh, 1998“An agent is an entity whose state is viewed as consisting of mental components such as beliefs, capabilities, choices, and commitments. [sic] In this view, therefore, agenthood is in the mind of the programmer.”Shoham, 19934Agent ProgramInputs = observationsObservations: states of the agent’s domain or environmentOutputs = actionsActions: Speak, Search, Move, BidAgent( o1, o2, … )( a1, a2, … )5Agents EnvironmentsAn agent must have a model of its domain and a model of other agents that it communicates with.Properties of agents’ environment:ObservableDynamicDiscrete6Basic CharacteristicsDelegation abilities: The owner or user of an agent delegates a task to the agent and the agent autonomously performs the task on behalf of the user.An agent can decompose and/or delegate the task to other agents;Once the task is complete the agent may need to report to the user/agent issuing the task.7Basic Characteristics (cont’d)Agent communication languages and protocols: information exchange with other agents establishes a need for expressive communication and negotiation language.KQML (Knowledge Query and Manipulation Language);Used to allow information agents to assert interests in information services, advertise their own services, and explicitly delegate tasks and requests for assistance from other agents.Can be used for developing a variety of inter-agent communication protocols that enable information agents to collectively cooperate.8Basic Characteristics (cont’d)Self-representation abilities: the ability to express business and system aspects of its functionality, combine them into an application or implementation.Self-describing, dynamic reconfigurable agents;Facilitate composition (specification and implementation) of large-scale (distributed) applications.9Agent Mental State (BDI)Beliefs–knowledge about the world and the effects the agent’s actions have on the world.Desires–preferences over possible states of the world (goals).Intentions–internal commitments made to achieve certain world states.10Example: Trading AgentUser preferencesAuctionrulesModel of other market participantsStrategysynthesizerBiddingstrategyMarket InfoBids11Example: Trading AgentBeliefs: auction rules, model of marketDesires: user preferencesIntentions: objects it has decided to buy/sellCapabilities: place new bidsObservations: market information12Reactive AgentLookup table maps each observation, or series of observations, to an actionan = f(on), oran = f(o1,…, on)FastInflexibleIntractable for nontrivial domains13Rational AgentsDecision theoretic (economic)Agent makes optimal decisions given its beliefs, goals, and intentions.LogicalAgent makes decisions that are consistent with its beliefs, goals, and intention.14Boundedly Rational AgentsAgent makes optimal decisions given its beliefs, goals, intentions, and the limits of its computational abilities.15Learning AgentAn agent that updates its beliefs based on its observationsWhat can we learn?Model of the worldNew capabilitiesEffects of our actions16Learning AgentLearning task:Learn , where sn+1 = (sn, an)Types of learningSupervisedReinforcementUnsupervised17AutonomyAgent autonomy, with respect toUser = execution autonomyOther agents = social autonomyDesigner autonomy, with respect toCommunication = interface autonomyArchitecture = design autonomyUtility function = preference autonomy18Belief RepresentationKnowledge levelThe Wolfline runs from HC to CCLogical level (declarative)Connects(Wolfline,HC,CC)Implementation level (procedural)public class Bus{public string start = “HC”;public string end = “CC”;}19Benefits of DeclaritivismModularitySemanticsInspectabilityLearnabilityProgrammability20Other Properties of AgentsLifespan: transient to long-livedModeling: of itself, the world, and other agentsMobility: stationary or mobileMemory: non to perfect recall21Agent SocietiesSoftware infrastructureSocial organization22Software InfrastructureCommunication ChannelsCommon OntologiesService agents (i.e. directory agent)23CommunicationACL = agent communication languageExample: KQMLContent messages: tell, query, reply, etc.Flow control: next, etc.Generally do not prescribe semantics24OntologiesDefine the semantics of communicationNotoriously difficultEmployee = everyone on payrollEmployee = everyone receiving benefits25Social OrganizationHomogeneous or heterogeneousSelf-interest v.s. cooperativeSocial control structureSystem evaluation26Emergent BehaviorAgents act With limited, local knowledgeIn self interestSystem behavesIn globally desirable mannerWithout central controlAdam Smith’s “invisible hand”27E-commerce exampleTrading agents, againHeterogeneousSelf-interestedMediatedGame-theoretic evaluations28Legacy System ExampleAgentDB1DB2DB3AgentAgentUserAgent29Personal AssistantsAgents that support a user’s taskExample (weak)Dialogue basedAnthropoidCooperative ?But has no goals of its own30Personal AssistantsExample: smart calendar/datebook that couldNegotiate appointments for meActively keep track of my contacts by searching the webLearn priorities for my mail31ConclusionAgenthood is a convenient descriptionAgents are described by beliefs, desires, and intentionsAgents
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