© 1999 Singh & Huhns1 Principles of Agents and Multiagent Systems Munindar P. Singh Michael N....

© 1999 Singh & Huhns 1

Principles of Agents and Multiagent Systems

Munindar P. Singh

Michael N. Huhns

[email protected]

[email protected]

http://www.csc.ncsu.edu/faculty/mpsingh/

http://www.ece.sc.edu/faculty/Huhns/


Tremendous Interest in Agent Technology

Evidence:• 400 people at Autonomous Agents 98

• 550 people at Agents World in Paris

Why?• Vast information resources now accessible• Ubiquitous processors• New interface technology• Problems in producing software


What is an Agent?

• The term agent in computing covers a wide range of behavior and functionality.

• 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)


The Agent Test

• “A system containing one or more reputed agents should change substantively if another of the reputed agents is added to the system.”


Social Engineering for Agents

Computers are making more and more decisions autonomously:

• when airplanes land and take off (fuel vs. tax)

• how phone calls are routed (pricing; choose carrier dynamically)

• how loads are controlled in an electrical grid• when packages are delivered• which stocks are bought and sold• electronic marketplaces


An Agent Should Act

• Benevolently• Predictably

– consistent with its model of itself

– consistent with its model of other agents’ beliefs about itself


Benevolence“A Mattress in the Road”

Mattresscars


A Collective Store

• Benevolent agents might contribute information they have retrieved, filtered, and refined to a collective store database

• Access to the collective store might be predicated on contributions to it

Collective Store Database World Wide Web...

Query Agents


Agent Behavior Testbed - University of South Carolina

= agent in cell = box in cell = target(+)nAan []n


Agents in a Cooperative Information System Architecture

E-MailSystem

WebSystem

DatabaseSystem

Application

ApplicationApplication

Application

(Mediators, Proxies, Aides, Wrappers)

Agent

Agent

Agent

Agent

Agent

Agent

Agent

Agent


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?


Dimensions of CIS: SystemScale is the number of agents:

Interactions:

Coordination (self interest):

Agent Heterogeneity:

Communication Paradigm:

Individual Committee Society

Reactive Planned

Antagonistic AltruisticCollaborative

Competitive Cooperative Benevolent

Identical Unique

Point-to-Point Multi-by-name/role Broadcast


Dimensions of CIS: Agent

Dynamism is the ability of an agent to learn:

Autonomy:

Interactions:

Sociability (awareness):

Fixed Teachable Autodidactic

Controlled Independent

Simple Complex

Interdependent

Autistic CollaborativeCommitting


Challenges

• Doing the "right" thingShades of autonomyConventions: emergence and maintenanceCoordinationCollaborationCommunication: semantics and pragmaticsInteraction-oriented programming


BASIC CONCEPTS


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”)


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


Agent Abstractions/1

• The traditional abstractions are from AI and are mentalistic– beliefs: agent’s representation of the world– knowledge: (usually) true beliefs– desires: preferred states of the world– goals: consistent desires– intentions: goals adopted for action



• The agent-specific abstractions are inherently interactional– social: about collections of agents– organizational: about teams and groups– ethical: about right and wrong actions– legal: about contracts and compliance



Inherently interactional

Agents, when properly understood

• lead naturally to multiagent systems

• provide a means to capture the fundamental abstractions that apply in all major applications and which are otherwise ignored by system builders


Agents versus AI

Traditional AI Agents

Entities Stand-alone Social: flexibleautonomy, communities,responsibility

Actions(in terms of)

Cause and effect Ethical concepts of rightand wrong

Contracts(in terms of)

Simplisticobligations

Directed relationshipscapturing rights, duties,powers, and liabilities.


How to Apply the Abstractions

Consider how the components of a any practical situation involving large and dynamic software systems. – Dynamism => autonomly– Openness and compliance => ability to enter

into and obey contracts– Trustworthiness => ethical behavior


Why Do These Abstractions Matter?

• Because of modern applications that demand going beyond traditional metaphors and models– virtual enterprises: manufacturing supply

chains, autonomous logistics, – electronic commerce: utility management– communityware: social user interfaces– problem-solving by teams


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



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



Goals

State


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


A Utility-Based Agent

Agent

En

vironm

ent

Sensors

Effectors



Utility

State


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


5. INTERACTION AND COMMUNICATION


Cognitive Economy

Prefer the simpler (more economical) explanation ("but not too simple" - Einstein)

Implications of Cognitive Economy:

• Agents must represent their environment

• Agents must represent themselves

• Agents must represent other agents ad infinitum

• Zero-order model: other agents are the same as oneself


CoordinationA property of interaction among a set of agents performing

some activity in a shared state. The degree of coordination is the extent to which they

avoid extraneous activity– reduce resource contention

– avoid livelock

• avoid deadlock

• maintain safety conditions

Cooperation is coordination among nonantagonistic agents. Typically,

• each agent must maintain a model of the other agents

• each agent must develop a model of future interactions


The Contract Net ProtocolAn important generic protocol• A manager announces the existence of tasks via a (possibly selective)

multicast

• Agents evaluate the announcement. Some of these agents submit bids

• The manager awards a contract to the most appropriate agent

• The manager and contractor communicate privately as necessary


Task Announcement Message

• Eligibility specification: criteria that a node must meet to be eligible to submit a bid

• Task abstraction: a brief description of the task to be executed

• Bid specification: a description of the expected format of the bid

• Expiration time: a statement of the time interval during which the task announcement is valid


Bid and Award Messages

• A bid consists of a node abstraction—a brief specification of the agent’s capabilities that are relevant to the task

• An award consists of a task specification—the complete specification of the task


Applicability of Contract Net

The Contract Net is• a high-level communication protocol

• a way of distributing tasks

• a means of self-organization for a group of agents

Best used when• the application has a well-defined hierarchy of tasks

• the problem has a coarse-grained decomposition

• the subtasks minimally interact with each other, but cooperate when they do


CONTROL


Goals for Multiagent Control

Develop Technologies for... • Locating and allocating capabilities and resources

that are dispersed in the environment• Predicting, avoiding, or resolving contentions over

capabilities and resources• Mediating among more agents, with more

heterogeneity and more complex interactions• Maintaining stability, coherence, and effectiveness


Control Challenges

What makes control difficult can be broken down into several major characteristics of the overall system, including:

• The Agents that comprise the system

• The Problems that those agents are solving individually and/or collectively

• The Solution characteristics that are critical


Control Challenges:AgentsControl is harder as agents are:

• More numerous

• More complex individually (e.g., more versatile)

• More heterogeneous in their capabilities, means of accomplishing capabilities, languages for describing capabilities, etc.

quantity

heterogeniety

complexity


Control Challenges:ProblemsControl is harder as the problems

agents solve are• More interrelated• Changing more rapidly, or

pursued in an uncertain and changing world

• More unforgiving of control failures (e.g., involving irreversible actions)

degree of interaction

severity of failure

volatility


Control Challenges:SolutionsControl is harder as solutions

to agent problems must be

• Better (e.g., more efficient) for the circumstances

• More robust to changing circumstances

• Cheaper/faster to develop individually and in concert

quality / efficiency

low overhead

robustness


Technologies for Agent Control

• Broker-based

• Matchmaker-based

• Market-based; auctions

• BDI and commitment based

• Decision theoretic

• Workflow (procedural) based

• Standard operating procedures

• Learning / adaptive

• Coordinated planning

• Conventions / protocols

• Stochastic or physics-based

• Organizations: teams and coalitions

• Constraint satisfaction/ optimization


Example Experiments: Capability Location

(1) Investigate matchmaking and distributed matchmaking complexities as a function of numbers of agents

(2) Investigate brokering vs. matchmaking vs. direct interaction as a function of different task types and allocation mechanisms

# of agents

Matchmaking activity

Task Type

Brokering

allocation mechanism

Matchmaking


Example Experiments: Capability Allocation and Scheduling

(1) Investigate quality/cost of allocating scarce capabilities as number of capabilities and their consumers rises

(2) Investigate quality/cost of scheduling reusable/nonsharable capabilities as volatility/uncertainty in agents’ future needs rises

# of agents

Allocation costs

volatility/uncertainty

scheduling mechanism

capability utilization


Parameters of Tasks and Experiments

• Number of tasks• Types of tasks

– number of resources

– duration of resource need

– complementarity/substitutability

– sequencing of resource needs

• Resource contention/overlap in needs• Types of resources

– reusable/sharable/scaleable


Dimensions of Control

Control howcapabilitiesare

Possibly by Why difficult? Why needed?

Allocated orscheduled

Markets, CSP,hierarchy,planning,teamwork

Many demands,scarce supply

Avoidcontention, useresources well

Located Brokers,matchmakers,markets,broadcast

Arrival/departure rate,variety, scale

Must findresources toemploy them




Not wasted Broker-monitoredrequests,caching,communication

Similar tasksarising variousplaces

Efficiency

Demanded Reacting toprices,replanning, goaladjustment

Number ofalternativechoices, selfinterest

Avoid contention

Supplied Reprogram ortrain agents,evolve/spawn,inject "friction"

Oscillations orchaos fromsporadic demand,allocatingprocesses

Adapt/grow withneed




Described Provide more orless detail, lumpor differentiatecapabilities

Rich space ofcapabilities,disagree on betterdescription,propagatingdescriptions

Description mustfollow use

Initiallyallocated

Organization orrole restructuring

Detecting need,assessingchoices,propagating

Choiceconstrains qualityof coordination

Differentiated Prevention(maintainconsistency),response (enlargecapabilitylanguage)

Continuousoperation, agentadaptation

Differentiationinevitable


SOCIAL ABSTRACTIONS


Social Abstractions

• Commitments: social, joint, collective, ...Organizations and rolesTeams and teamworkMutual beliefs and problemsJoint intentionsPotential conflict with individual rationality


Coherence and Commitments

• Coherence is how well a system behaves as a unit. It requires some form of organization, typically hierarchical

• Social commitments are a means to achieve coherence

© 1999 Singh & Huhns

Example: Electronic Commerce

• Define an abstract sphere of commitment (SoCom) consisting of two roles: buyer and seller, which require capabilities and commitments about, e.g.,– requests they will honor– validity of price quotes

• To adopt these roles, agents must have the capabilities and acquire the commitments.

Buyer and Seller Agents

SoComs provide the context for the concepts represented & communicated.


Example: Electronic Commerce

• Agents can join– during execution—requires publishing the

definition of the commerce SoCom– when configured by humans

• The agents then behave according to the commitments

• Toolkit should help define and execute commitments, and detect conflicts.

Virtual Enterprises (VE)

Two sellers come together with a new proxy agent called VE.

Example of VE agent commitments:

• notify on change

• update orders

• guarantee the price

• guarantee delivery date

VE and EC Composed


Social Commitments

• Operations on commitments (instantiated as social actions):– create– discharge (satisfy)– cancel– release (eliminate)– delegate (change debtor)– assign (change creditor).


Policies and Structure

• Spheres of commitment (SoComs)– abstract specifications of societies– made concrete prior to execution

• Policies apply on performing social actions

• Policies related to the nesting of SoComs

• Role conflicts can occur when agents play multiple roles, e.g., because of nonunique nesting.


ETHICAL ABSTRACTIONS


Ethical Abstractions

• UtilitarianismConsequentialismObligationsDeontic logicParadoxes


Motivation

The ethical abstractions help us specify agents who act appropriately.

• Intuitively, we think of ethics as just the basic way of distinguishing right from wrong.

• It is difficult to entirely separate ethics from legal, social, or even economic considerations


Right and Good

• Right: that which is right in itself

• Good: that which is good for someone or for some end


Deontological vs Teleological

• Deontological theories– right before good– being good does not mean being right– ends do not justify means

• Teleological theories– good before right– right maximizes good– ends justify means


Deontological Theories

• Constraints– negatively formulated– narrowly framed

• e.g., lying is not not-telling-the-truth

– narrowly directed at the agent’s specific action • not its occurrence by other means

• not the consequences that are not explicitly chosen


Teleological Theories

• Based on how actions satisfy various goals, not their intrinsic rightness

• comparison-based

• preference-based


Consequentialism


Utilitarianism

This is the view that a moral action is one that is useful

• must be good for someone• good may be interpreted as

– pleasure: hedonism

– preference satisfaction: microeconomic rationalism (assumes each agent knows its preferences)

– interest satisfaction: welfare utilitarianism

– aesthetic ideals: ideal utilitarianism


Obligations

• For deontological theories, obligations are those that are impermissible to omit


Applying Ethics

• The deontological theories– are narrower– ignore practical consideration– but are only meant as incomplete constraints (of all right

actions, the agent can choose any)

• The teleological theories– are broader– include practical considerations– but leave fewer options for the agent, who must always

choose the best available alternative


LEGAL ABSTRACTIONS


Legal Abstractions

• ContractsDirected obligationsHohfeldian concepts: right, duty, power, liability, immunity, ...Following protocolsDefining and testing compliance


UNDERSTANDING COMMUNICATION


Interaction and Communication

• Interactions occur when agents exist and act in close proximity:– resource contention, e.g., bumping into each other

• Communication occurs when agents send messages to one another with a view to influencing beliefs and intentions. Implementation details are irrelevant:

• can occur over communication links– can occur through shared memory

– can occur because of shared conventions


Speech Act TheorySpeech act theory, initially meant for natural language, views

communications as actions. It considers three aspects of a message:

• Locution, or how it is phrased, e.g.,– "It is hot here" or "Turn on the cooler"

• Illocution, or how it is meant by the sender or understood by the receiver, e.g.,– a request to turn on the cooler or an assertion about the temperature

• Perlocution, or how it influences the recipient, e.g.,– turns on the cooler, opens the window, ignores the speaker

Illocution is the main aspect.


Syntax, Semantics, Pragmatics

For message passing

• Syntax: requires a common language to represent information and queries, or languages that are intertranslatable

• Semantics: requires a structured vocabulary and a shared framework of knowledge-a shared ontology

• Pragmatics:– knowing whom to communicate with and how to find them

– knowing how to initiate and maintain an exchange

– knowing the effect of the communication on the recipient


KQML Semantics

• Each agent manages a virtual knowledge base (VKB)

• Statements in a VKB can be classified into beliefs and goals

• Beliefs encode information an agent has about itself and its environment

• Goals encode states of an agent’s environment that it will act to achieve

• Agents use KQML to communicate about the contents of their own and others’ VKBs


Semantics of Communications

What if the agents have• different terms for the same concept?• same term for different concepts?• different class systems or schemas?• differences in depth and breadth of coverage?


Common Ontologies• A shared representation is essential to successful

communication and coordination

• For humans, this is provided by the physical, biological, and social world

• For computational agents, this is provided by a common ontology:– terms used in communication can be coherently defined

– interaction policies can be shared

• Current efforts are– Cyc

– DARPA ontology sharing project

– Ontology Base (ISI)

– WordNet (Princeton)


ECONOMIC ABSTRACTIONS


Motivation

The economic abstractions have a lot of appeal as an existing approach to capture complex systems of autonomous agents.

• By themselves they are incomplete

• Can provide a basis for achieving some of the contractual behaviors, especially in – helping an agent decide what to do– helping agents negotiate.


Market-Oriented Programming

• An approach to distributed computation based on market price mechanisms

• Effective for coordinating the activities of many agents with minimal communication

• Goal: build computational economies to solve problems of distributed resource allocation


Benefits

• For agents, the state of the world is described completely by current prices

• Agents do not need to consider the preferences or abilities of others

• Communications are offers to exchange goods at various prices

• Under certain conditions, a simultaneous equilibrium of supply and demand across all of the goods is guaranteed to exist, to be reachable via distributed bidding, and to be Pareto optimal.


Market Behavior

• Agents interact by offering to buy or sell quantities of commodities at fixed unit prices

• At equilibrium, the market has computed the allocation of resources and dictates the activities and consumptions of the agents


Agent Behavior

• Consumer agents: exchange goods

• Producer agents: transform some goods into other goods

• Assume individual impact on market is negligible

• Both types of agents bid so as to maximize profits (or utility)


Principles of Negotiation

• Negotiation involves a small set of agents• Actions are propose, counterpropose, support, accept,

reject, dismiss, retract• Negotiation requires a common language and common

framework (an abstraction of the problem and its solution)• RAD agents exchange DTMS justifications and class

information• Specialized negotiation knowledge may be encoded in

third-party agents• The only negotiation formalism is unified negotiation

protocol [Rosenschein, Hebrew U.]


Negotiation

• A deal is a joint plan between two agents that would satisfy both of their goals

• The utility of a deal for an agent is the amount he is willing to pay minus the cost to him of the deal

• The negotiation set is the set of all deals that have a positive utility for every agent

The possible situations for interaction are• conflict: the negotiation set is empty• compromise: agents prefer to be alone, but will agree to a negotiated

deal• cooperative: all deals in the negotiation set are preferred by both

agents over achieving their goals alone[Rosenschein and Zlotkin, 1994]


Negotiation Mechanism

The agents follow a Unified Negotiation Protocol, which applies to any situation. In this protocol,

• the agents negotiate on mixed-joint plans, i.e., plans that bring the world to a new state that is better for both agents

• if there is a conflict, they "flip a coin" to decide which agent gets to satisfy his goal


Negotiation Mechanism Attributes

• Efficiency

• Stability

• Simplicity

• Distribution

• Symmetry

e.g., sharing book purchases, with cost decided by coin flip


Third-Party Negotiation• Resolves conflicts among antagonistic agents directly or

through a mediator• Handles multiagent, multiple-issue, multiple-encounter

interactions using case-based reasoning and multiattribute utility theory

• Agents exchange messages that contain– the proposed compromise– persuasive arguments– agreement (or not) with the compromise or argument– requests for additional information– reasons for disagreement– utilities / preferences for the disagreed-upon issues

[Sycara]


Negotiation in RAD

• Resolves conflicts among agents during problem solving

• To negotiate, agents exchange– justifications, which are maintained by a DTMS

– class information, which is maintained by a frame system

• Maintains global consistency, but only where necessary for problem solving


Negotiation amongUtility-Based Agents

Problem: How to design the rules of an environment so that agents interact productively and fairly, e.g.,

• Vickrey’s Mechanism: lowest bidder wins, but gets paid second lowest bid (this motivates telling the truth?? and is best for the consumer??)


Problem Domain Hierarchy

Worth-Oriented Domains

State-Oriented Domains

Task-Oriented Domains


Task-Oriented Domains

• A TOD is a tuple <T, A, c>, where T is the set of tasks, A is the set of agents, and c(X) is a monotonic function for the cost of executing the set of tasks X

• Examples– delivery domain: c(X) is length of minimal path that visits X

– postmen domain: c(X) is length of minimal path plus return

– database queries: c(X) is minimal number of needed DB ops


TODs

• A deal is a redistribution of tasks

• Utility of deal d for agent k isUk (d) = c(Tk) - c(dk)

• The conflict deal, D, is no deal

• A deal d is individual rational if d>D

• Deal d dominates d’ if d is better for at least one agent and not worse for the rest

• Deal d is Pareto optimal if there is no d’>d

• The set of all deals that are individual rational and Pareto optimal is the negotiation set, NS


Monotonic Concession Protocol

• Each agent proposes a deal

• If one agent matches or exceeds what the other demands, the negotiation ends

• Else, the agents propose the same or more (concede)

• If no agent concedes, the negotiation ends with the conflict dealThis protocol is simple, symmetric, distributed, and guaranteed to end in a finite number of steps in any TOD. What strategy should an agent adopt?


Zeuthen Strategy

Offer deal that is best among all deals in NS• Calculate risks of self and opponent

R1=(utility A1 loses by accepting A2’s offer) (utility A1 loses by causing a conflict)

• If risk is smaller than opponent, offer minimal sufficient concession (a sufficient concession makes opponent’s risk less than yours); else offer original deal

• If both use this strategy, they will agree on deal that maximizes the product of their utilities (Pareto optimal)

• The strategy is not stable (when both should concede on last step, but it’s sufficient for only one to concede, then one can benefit by dropping strategy)


Deception-Free Protocols

• Zeuthen strategy requires full knowledge of– tasks

– protocol

– strategies

– commitments

• Hidden tasks• Phantom tasks• Decoy tasks

P.O. A1 (hidden)

A1 A2


8. SYNTHESIS


Research Trends

• Economic: Sycara, Rosenschein, Sandholm, Lesser

• Social: organizational theory and open systems—Hewitt, Gasser, Castelfranchi

• Ethical:

• Legal:

• Communication:

• Coordination:

• Collaboration:

• Formal Methods—Singh, Wooldridge, Jennings, Georgeff


Interaction-Oriented Software Development

• Active modules, representing real-world objects

• Declarative specification (“what,” not “how”)

• Modules that volunteer

• Modules hold beliefs about the world, especially about themselves and others


What is IOP?

• A collection of abstractions and techniques for programming MAS.

• Classified into three layers of mechanisms :– coordination: living in a shared environment– commitment: organizational or social coherence

(adds stability over time)– collaboration: high-level interactions

combining mental and social abstractions.


IOP Contribution

• Enhances and formalizes ideas from different disciplines

• Separates them out in an explicit conceptual metamodel to use as a basis for programming and for programming methodologies

• Makes them programmable


Benefits of IOP

• Like all conceptual modeling, IOP offers a higher-level starting point than traditionally available. Specifically:– key concepts of coordination, commitment,

collaboration as first-class concepts that can be applied directly

– aspects of the underlying infrastructure are separated, leading to improved portability.


Representations for IOP

• Functionalities, which typically exist– effected by humans in some unprincipled way– hard-coded in applications– buried in operating procedures and manuals

• Information, which typically exists– in data stores– in the environment or with interacting entities.

Problem: interactive aspects are not modeled.


Lessons• Advanced abstractions

– must be simple

– must reflect true status


Challenges

• Formal semantics

• Operational semantics related to formal semantics

• Tools

• Design rules capturing useful patterns, but respecting the formal semantics


To Probe Further• Readings in Agents (Huhns & Singh, eds.), Morgan

Kaufmann, 1997

http://www.mkp.com/books_catalog/1-55860-495-2.asp

• [email protected]

• Journal of Autonomous Agents and Multiagent Systems

• International Conference on Multiagent Systems (ICMAS)

• International Joint Conference on Artificial Intelligence

• International Workshop on Agent Theories, Architectures, and Languages (ATAL)

© 1999 Singh & Huhns1 Principles of Agents and Multiagent Systems Munindar P. Singh Michael N....

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Transcript of © 1999 Singh & Huhns1 Principles of Agents and Multiagent Systems Munindar P. Singh Michael N....