OVERVIEW OF AGENTSAND AGENT

ENVIRONMENTS

Categories of Agent Research

HumanIntelligence

IdealIntelligence

Reasoning Agents that think likehumans(cognitive science)

Agents that thinkrationally(logic)

Behavior Agents that act likehumans(Turing test)

Agents that behaverationally(“do the right thing”)

What Is an Agent?

In general, an agent is an active computational entity• with a persistent identity• that can perceive, reason about, and initiate

activities in its environment• that can communicate (with other agents)

Roles of AgentsAgents can serve several roles in information systems. Each

role can have different variants• As client

– does everything itself– tells a server how to do something– tells a server what the client would like to have done

• As server– does nothing– does exactly as told– satisfies high-level requests– preserves self-interest

• As contents of messages– embodies all of client's functionality– is a procedural script– is a declarative specification

Agent Characteristics/1

• Locality: local or remote• Uniqueness: homogeneous or heterogeneous• Granularity: fine- or coarse-grained• Persistence: transient or long-lived• Level of Cognition: reactive or deliberative• Sociability: autistic, aware, responsible, team player• Friendliness: cooperative or competitive or antagonistic• Construction: declarative or procedural• Semantic Level: communicate what or how• Mobility: stationary or itinerant

Agent Characteristics/2• Autonomy: independent or controlled• Adaptability: fixed or teachable or autodidactic• Sharing: degree and flexibility with respect to

– communication: vocabulary, language, protocol– intellect: knowledge, goals, beliefs, specific ontologies– skills: procedures, "standard" behaviors, implementation

languages

• Interactions: direct or via facilitators, mediators, or “nonagents”

• Interaction Style/Quality/Nature: with each other or with “the world”, or both?

• Do the agents model their environment, themselves, or other agents?

A Rational Agent

Rationality depends on...• The performance measure for success• What the agent has perceived so far• What the agent knows about the environment• The actions the agent can perform

An ideal rational agent: for each possible percept sequence, it acts to maximize its expected utility, on the basis of its knowledge and the evidence from the percept sequence

A Simple Reactive Agent

Agent

En

vironm

ent

Sensors

Effectors

What the worldis like now

What action Ishould do now

Condition-action rules

A Simple Reactive Agent

function Simple-Reactive-Agent(percept)static: rules, a set of condition-action rules

state Interpret-Input(percept)rule Rule-Matching(state, rules)action Rule-Action(rule)return action

A Reactive Agent with State

Agent

En

vironm

ent

Sensors

Effectors

What the worldis like now

What action Ishould do now

Condition-action rules

State

How the world evolves

What my actions do

function Reactive-Agent-with-State(percept) static: rules, a set of condition-action rules state, a description of the current world state Update-State(state, percept) rule Rule-Matching(state, rules) action Rule-Action(rule) state Update-State(state, action) return action

A Reactive Agent with State

A Goal-Based Agent

Agent

En

vironm

ent

Sensors

Effectors

What the worldis like now

What action Ishould do now

Goals

State

How the world evolves

What my actions doWhat it will be likeif I do action A

A Utility-Based Agent

Agent

En

vironm

ent

Sensors

Effectors

What the worldis like now

What action Ishould do now

Utility

State

How the world evolves

What my actions doWhat it will be likeif I do action A

How happy I willbe in such a state

A Utility-Based Agent

function Utility-Based-Agent(percept)static: a set of probabilistic beliefs about the state of the world

Update-Probs-for-Current-State(percept,old-action)Update-Probs-for-Actions(state, actions)Select-Action-with-Highest-Utility(probs)return action

Agent Environments• Communication Infrastructure

– Shared memory (blackboard)– Connected or Connectionless (email)– Point-to-Point, Multicast, or Broadcast– Directory Service

• Communication Protocol– KQML– HTTP and HTML– OLE, CORBA, DCOM, etc.

• Interaction Protocol• Mediation Services• Security Services (timestamps/authentication/currency)• Remittance Services• Operations Support

(archiving/billing/redundancy/restoration/accounting)

Agent Environments

• Accessible vs. Inaccessible• Deterministic vs. Nondeterministic• Episodic vs. Nonepisodic• Static vs. Dynamic• Discrete vs. Continuous

Open information environments (e.g., InfoSleuth) are inaccessible, nondeterministic, nonepisodic, dynamic, and discrete

Mediators

Modules that exploit encoded knowledge about data to create information for higher-level applications. Mediators, thus,

• provide logical views of the underlying information

• reside in an active layer between applications and resources

• are small, simple, and maintainable independently of others

• are declaratively specified, where possible, and inspectable by users

• come in a wide range of capabilities, from database and protocol converters, to intelligent modules that capture the semantics of the domain and learn from the data

Mediator ArchitectureApplication Programs

Information Resources

User Interfaces

NetworksNetwork Interfacesand Mediators

Mediator Interfaces

• Mediators should be separate from databases– mediators contain knowledge beyond the scope of a database– mediators contain abstractions that are not part of a database– mediators must deal with uncertainty– mediators access multiple databases to combine disjoint data

• Mediators should be separate from applications– their functions are different in scope than those of applications– separate mediators are easier to maintain

• Because mediators are stable and small, they can be mobile– they can be shipped to sites where large volumes of data must be

processed

Learning in Mediators

Learning can be driven by• feedback from performance measures• explicit induction over information resources

Result of learning can be• modifications to certainty parameters• augmented tabular knowledge• new symbolic concepts

Type BrokersA means to manage structure and semantics of information

and query languages. Define standard types by which computations can communicate. Most of this work pertains to lower level issues than CIS

Typically these involve a set of type servers or brokers and a way to distribute type information. An application uses the broker to find a service, and then communicates directly with the desired service

Brokers give slightly more semantics than directories--the type signature of methods, not just their names

With more sophisticated notions of service semantics, these could be more useful

Protocol Handlers• Mediators [Wiederhold]

• Aides [Carnot DCA]

• Database and Protocol Agents [Carnot ESS]

• Heads [Steiner]

• Brokers [OMNI]

• Knowledge handlers [COSMO]

• Intelligent information agents [Papazoglou]

• Front-end processors [Hecodes]

• Integrating agents, routers, and wrappers [Gray]

• Facilitators [ARPA Knowledge Sharing Effort]