An Introduction to Agent Technologies
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Transcript of An Introduction to Agent Technologies
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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