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An Introduction to Agent Technologies

Peter Wurman, NCSUYelena Yesha, UMBCOlga Streltchenko, UMBC

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Presentation Overview

Working definition of an agentAgent characteristics and propertiesAgent societiesExamples

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Working 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, 1993

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Agent Program

Inputs = observations Observations: states of the agent’s domain or

environment

Outputs = actions Actions: Speak, Search, Move, Bid

Agent

( o1, o2, … ) ( a1, a2, … )

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Agents Environments

An agent must have a model of its domain and a model of other agents that it communicates with.

Properties of agents’ environment: Observable Dynamic Discrete

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Basic Characteristics

Delegation 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.

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Basic 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.

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Basic 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.

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Agent 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.

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Example: Trading Agent

User preferences

Auctionrules

Model of other market participants

Strategysynthesizer

Biddingstrategy

Market Info

Bids

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Example: Trading Agent

Beliefs: auction rules, model of market

Desires: user preferencesIntentions: objects it has decided to

buy/sellCapabilities: place new bidsObservations: market information

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Reactive Agent

Lookup table maps each observation, or series of observations, to an action

an = f(on), oran = f(o1,…, on)

Fast Inflexible Intractable for nontrivial domains

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Rational Agents

Decision theoretic (economic) Agent makes optimal decisions given its

beliefs, goals, and intentions.Logical

Agent makes decisions that are consistent with its beliefs, goals, and intention.

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Boundedly Rational Agents

Agent makes optimal decisions given its beliefs, goals, intentions, and the limits of its computational abilities.

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Learning Agent

An agent that updates its beliefs based on its observations

What can we learn? Model of the world New capabilities Effects of our actions

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Learning Agent

Learning task: Learn , where sn+1 = (sn, an)

Types of learning Supervised Reinforcement Unsupervised

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Autonomy

Agent autonomy, with respect to User = execution autonomy Other agents = social autonomy

Designer autonomy, with respect to Communication = interface autonomy Architecture = design autonomy Utility function = preference autonomy

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Belief Representation

Knowledge level The Wolfline runs from HC to CC

Logical level (declarative) Connects(Wolfline,HC,CC)

Implementation level (procedural)public class Bus{

public string start = “HC”;public string end = “CC”;}

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Benefits of Declaritivism

ModularitySemanticsInspectabilityLearnabilityProgrammability

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Other Properties of Agents

Lifespan: transient to long-livedModeling: of itself, the world, and

other agentsMobility: stationary or mobileMemory: non to perfect recall

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Agent Societies

Software infrastructureSocial organization

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Software Infrastructure

Communication ChannelsCommon OntologiesService agents (i.e. directory agent)

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Communication

ACL = agent communication language

Example: KQML Content messages: tell, query, reply, etc.

Flow control: next, etc.Generally do not prescribe semantics

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Ontologies

Define the semantics of communication

Notoriously difficult Employee = everyone on payroll Employee = everyone receiving benefits

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Social Organization

Homogeneous or heterogeneousSelf-interest v.s. cooperativeSocial control structureSystem evaluation

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Emergent Behavior

Agents act With limited, local knowledge In self interest

System behaves In globally desirable manner Without central control

Adam Smith’s “invisible hand”

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E-commerce example

Trading agents, again Heterogeneous Self-interested Mediated Game-theoretic evaluations

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Legacy System Example

Agent

DB1 DB2 DB3

AgentAgent

UserAgent

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Personal Assistants

Agents that support a user’s task Example (weak)

Dialogue basedAnthropoidCooperative ?But has no goals of its own

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Personal Assistants

Example: smart calendar/datebook that could Negotiate appointments for me Actively keep track of my contacts by

searching the web Learn priorities for my mail

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Conclusion

Agenthood is a convenient descriptionAgents are described by beliefs, desires,

and intentionsAgents select actions based on

observationsCooperating agents are a form of

distributed computationSelf-interested agents can generate

desirable emergent behavior