Agent Technology for e-Commerce Chapter 3: Multi-agent Systems Maria Fasli

Agent Technology for e-Commerce

Chapter 3: Multi-agent Systems

Maria Fasli

http://cswww.essex.ac.uk/staff/mfasli/ATe-Commerce.htm

2Chapter 3


MASs definition

Definition: A multi-agent system (MAS) consists of a network of loosely-coupled computational autonomous agents who can perform actions, they have resources at their disposal and they possess knowledge, capabilities or skills. They are situated in a common environment and they can interact through a set of rules, namely an interaction protocol.

It is rare for an agent to act in isolation and it is even more rare for an agent to be useful on its own

3Chapter 3


Single agent-based systems differ from multi-agent systems: Environment: agents need to take into account others who may

interfere with their plans and goals. They need to coordinate with others in order to avoid conflicts.

Knowledge/expertise/skills: these are distributed Design: agents need not be homogeneous and may be designed

and implemented using different methodologies and languages Interaction: agents interact following rules of interaction

(interaction protocols)

4Chapter 3


Coherence

Coherence, i.e. how well the system behaves as a unit, is important in MASs

Coherence can be measured in different ways depending on the problem domain and system

Usually measured from an external observer’s perspective who ascertains whether or not a system appears to be behaving in a coherent way – not necessarily something that the agents themselves are aware of

5Chapter 3


MASs applications

They are particularly suitable for: Building systems to solve complex problems that cannot be

solved by any one agent on its own Dealing with problems that involve many problem-solving

methods, require different types of expertise and knowledge or where there are multiple viewpoints

Creating systems where dynamic reorganization is required Tasks in which the information resources are distributed

6Chapter 3


MASs advantages

The use of MASs technology offers a number of advantages: Extensibility and flexibility Robustness and reliability Computational efficiency and speed Development and maintainability Reusability Reduced costs

7Chapter 3


MAS challenges

A number of issues with regards to their design and implementation: Efficient and effective interaction protocols Task and problem formulation, decomposition, task allocation to

individual agents and subtask synthesis Agent identification, search and location in open systems Coherent and stable system behaviour while avoiding harmful

interactions Representation of information about the state of the environment,

as well as other agents, their actions and knowledge Team and organization formation Efficient planning and learning algorithms

8Chapter 3


Closed MASs

Static design with pre-defined components and functionalities The properties of the system are known in advance:

common language each agent can be developed as an expert agents are cooperative multiple developers can work towards the development of the

system at the same time Example: a MAS in an organization

9Chapter 3


Advantages Distributed load and expertise Simplicity and predictability, since

components are known interaction language and protocols are known agents usually are cooperative agents share architecture and software

Disadvantages Maintenance costs can be high May be less fault tolerant Difficult to inter-operate with other systems

10Chapter 3


Open MASs

The system has no prior static design, only single agents within Agents are not necessarily aware of others – a mechanism for

identifying, searching and locating others is required Agents may be non-cooperative, malicious or not trustworthy Example: open electronic marketplaces

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Advantages Single agent or groups are designed separately (modular) Flexible and fault tolerant Evolutionary design Easier to maintain Dynamic, open society

Disadvantages Overall behaviour of the system not predictable Protocols, languages, ontologies may vary across agents Malicious behaviour difficult to avoid

12Chapter 3


Interaction

Interactions develop as a result of a series of actions whose consequences influence the future behavior of agents

May be direct or indirect, intended, or unintended Interaction assumes:

Agents that are capable of acting and/or communicating Situations that promote interaction Dynamic elements allowing for local and temporary

relationships among agents

Definition: An interaction occurs when two or more agents are brought into a dynamic relationship through a set of reciprocal actions (Ferber 1999)

13Chapter 3


Elements of interactions

Goals: The agents’ objectives and goals are important. Goals of different agents can be conflicting

Resources: agents require resources to achieve their objectives. Resources are not infinite, other agents may want to use them as well. Conflicts may arise

Expertise/skills/capabilities. Agents may lack the necessary skills, expertise of capabilities for accomplishing one or more of their tasks. They may require the ‘help’ of others.

14Chapter 3


Agent characterization

Self-interested/antagonistic agents: have incompatible goals with others and they are interested in maximizing their own utility, not necessarily that of the society as a whole

Cooperative/nonantagonistic agents: have usually compatible goals; they act to maximize their own utility in conjunction with that of the entire system

15Chapter 3


Modes of interaction

16Chapter 3


Interaction protocols

Interaction protocols govern the exchange of a series of messages among agents

Communication protocols provide rules that structure message-passing and produce meaningful dialogues or conversations

Cooperation protocols provide a framework within which agents can coordinate their actions to achieve a complex task or solve a difficult problem in a cooperative way

Negotiation protocols are used in situations where agents have incompatible goals to enable the parties involved to reach a compromise and resolve conflict

17Chapter 3


Agent communication

Why is communication important? Communication is required in MASs where agents have to

cooperate, negotiate etc., with other agents Agents need to communicate in order to understand and be

understood by others Diversity introduces heterogeneity

Can we use natural language as an Agent Communication Language (ACL)? No, natural language is ambiguous

18Chapter 3


Aspects of communication

Communication has three aspects Syntax: how the symbols of communication are structured Semantics: what the symbols denote Pragmatics: how the symbols are interpreted

Meaning: A combination of semantics and pragmatics

An agent can have an active or passive role or even both An agent can function as a master, a slave or a peer respectively

19Chapter 3


Speech Act Theory

Introduced by Austin (1962) and extended by Searle (1969) Communication exchange is a form of action: a speech act An utterance has three aspects

Locution: the physical utterance with context and reference Illocution: the act of conveying intentions Perlocution: the action that occurs as a result of the illocution

Identifying the intent or illocutionary force of an utterance is important

20Chapter 3


Illocutionary Force Categories

Assertive: statements of fact Commissive: commitments Directive: commands in a master-slave structure Declarative: statements of fact Expressive: expressions of emotion

21Chapter 3


Communication protocols

An agent communication language is defined at three levels: Lower level: specifies method of interconnection Middle level: specifies format/syntax Top level: specifies meaning/semantics

Communication protocols need to separate: semantics of the protocol which must be domain-independent semantics of the enclosed message which may be domain-

dependent

22Chapter 3


ACL components

An agent communication language consists of three components: An ‘outer’ language (pragmatics): the language used to express

the primitives, i.e. the performatives An ‘inner’ language (syntax): the logical or representation

language which is used to write the message itself Vocabulary (semantics): describes the domain of discourse in

terms of concepts and their relationships and prescribes meaning to the terms used

23Chapter 3


Knowledge and Query Manipulation Language

KQML is a high level, message-oriented, communication language and protocol for information exchange

An outer language There is a separation of protocol semantics from content

semantics All the information required to understand the message is

included in the message KQML is independent of:

the transport mechanism (tcp/ip, corba etc.) the content language (KIF, SQL etc.) the ontology assumed by the content

24Chapter 3


KQML message structure

Message structure

(KQML-performative :sender <word> :receiver <word> :in-reply-to <word> :language <word> :ontology <word> :content <expression>

…)

Example

(tell :sender Agent435 :receiver Agent450 :in-reply-to id-msg-005 :language KIF :ontology cycbase :content (salary 9876543 32765))

• Reserved keywords for performatives and parameters• The performatives are organized into several categories

25Chapter 3


KQML semantics

Semantics to KQML performatives is given in terms of preconditions, postconditions and completion conditions

Semantics is described in a language which includes operators for expressing an agent’s attitudes: beliefs (BEL), knowledge (KNOW), desires (WANT) and intentions (INT)

26Chapter 3


If A and B are the sender and the receiver respectively Pre(A) and Pre(B) identify the necessary states for sending and

receiving and processing a performative respectively Post(A) and Post(B) determine the states of the agents after the

successful utterance of the performative and its receipt Completion describes the final state after the conversation has

taken place and the original intention for uttering the performative has been accomplished

27Chapter 3


Semantics of tell

tell(A,B,X)

Pre(A): BEL(A,X)KNOW(A,WANT(B,KNOW(B,S)))

Pre(B): INT(B,KNOW(B,S))

where S may be any of BEL(B,X) or BEL(B,X)

Post(A): KNOW(A,KNOW(B,BEL(A,X)))

Post(B): KNOW(B,BEL(A,X))

Completion: KNOW(B,BEL(A,X))

28Chapter 3


Criticisms of KQML

KQML has been criticized on a number of fronts: Some of its performatives (achieve, broker, stream-all) are not

really speech acts Lack of commissive performatives No security model

29Chapter 3


FIPA ACL

FIPA ACL was part of FIPA’s standardization efforts for interaction protocols

Based on speech acts, messages are communicative acts It upkeeps the distinction between outer and inner languages and

is independent of the inner language A number of communication exchange protocols based on the

FIPA ACL communicative acts have been defined

30Chapter 3


Features of FIPA ACL

All communicative acts can be defined compositionally in terms of two core ones: inform and request

The syntax of performatives is similar to that of KQML

(inform :sender Agent435 :receiver Agent450 :language Prolog :ontology companyx :content “salary(9876543,32765)”)

31Chapter 3


FIPA ACL semantics

The agents’ states are defined via SL (Semantic Language) Semantics is based on mental attitudes such as belief, desires and

intentions etc. The meaning of primitives is given in terms of Feasibility

Conditions (FPs) and Rational Effects (REs)

32Chapter 3


FP(a) describes the necessary conditions that need to hold true in order for the sender to send communicative act a

RE(a) represents the effect that an agent can expect to occur as a result of performing the act (the purpose of the message) – the RE of a communicative act cannot be guaranteed as the receiver of the message is autonomous and may not conform

<i,inform(j,)> FP: Bi()Bi(Bifj()Uifj()) RE: Bj()

33Chapter 3


Criticisms of FIPA ACL

Lack of security mechanism (though a proposal had been submitted)

No facilitation primitives

34Chapter 3


Comparing KQML and FIPA ACL

Syntactically similar and both ascribe to the same theory Semantics differ:

different ways to describe the primitives, i.e. pre, post, completion conditions for KQML, Feasibility Conditions and Rational Effects for FIPA ACL

different language to describe the propositional mental attitudes, e.g. KQML’s BEL operator is not the same as FIPA ACL’s B operator

Different treatment of the ‘administration’ primitives

35Chapter 3


Content Languages

Agents need to be able to represent and reason about: models of other agents, their beliefs, knowledge, perceptions tasks, task structure, planning, goals

A common language for representing these things is needed (content language)

Examples of content languages: SQL, Prolog, KIF

36Chapter 3


Knowledge Interchange Format (KIF)

KIF is a content language based on first order logic Highly expressive and can be used to represent knowledge in

declarative form in knowledge bases Human readable

37Chapter 3


KIF syntax and semantics

KIF is a prefix version of first order predicate calculus Standard logical operators as well as quantification

(exists ((?x bird) (?y tree)) (on ?x ?y) One can define objects, relations between objects and rules

(defrelation mother(?x ?y):= (and (female ?x) (parent ?x ?y))) Knowledge about knowledge can be expressed via the (‘) and (,)

operators

(interested agent435 ‘(distance ,?x ,?y ,?z))

Model-theoretic semantics

38Chapter 3


Issues

KIF is probably the most widely used content language for knowledge bases

Highly expressive, but: complicates the building of fully conforming systems resulting systems tend to be heavyweight

Its logic-oriented format may not be acceptable to a wider community

39Chapter 3


Dialogues

A series of messages being exchanged comprise a dialogue or conversation

40Chapter 3


A layered model of communication

To enable effective agent communication among agent platforms a layered approach is required

Network Infrastructure

Transport and Signalling (TCP, UDP)

Message Transport Protocol (IIOP, HTTP, RMI)

Message Envelope (FIPA message envelope)

Agent Communication Language (FIPA ACL, KQML)

Content Language (KIF, Prolog, SQL)

Dialogue (Auctions, ContractNet, SNP)

41Chapter 3


Ontologies: The problem

Different agent developers diversity/heterogeneity of agents Agents may refer to the same concept under different terms

Client: anyone who buys products Customer: anyone who buys products

Agents may refer to different concepts under the same term Client: a computer in the client-server architecture Client: anyone who buys products

42Chapter 3


A shared representation is essential for successful communication and interaction between perhaps heterogeneous agents to take place

An ontology is a specification of the objects, concepts, and relationships in an area of interest

An ontology represents a domain from a particular perspective

43Chapter 3


Ontologies are useful: They define a common vocabulary for those who need to share

information They enable the reuse of domain knowledge They allow us to encode assumptions about a domain explicitly –

these can then easily be changed by simply changing the ontology

They allow us to separate domain from operational knowledge They can be used by people, software agents, databases and other

computer systems

44Chapter 3


Explicit ontologies

Domain knowledge can be encoded implicitly in procedures or explicitly in declarative statements

An explicit ontology is a formal description usually in a logical language of the concepts in a domain of discourse, their properties and relations

Focus is on classes which describe concepts

45Chapter 3


Three aspects Conceptualization: describes the underlying model of the domain

in terms of classes, objects, attributes and relations Vocabulary: assigns symbols or terms to refer to the classes,

objects, attributes and relations Axiomatization: encodes rules and constraints capturing

significant aspects of the domain model

46Chapter 3


Developing ontologies

Ontology development is done in a number of steps:

1. Determine the domain and the scope of the ontology as well as the purpose for using it

2. Organize the ontology and design the overall conceptual structure of the domain (pivotal in ontology design and development) Top-down approach: starts with general concepts and

proceeds with specialization Bottom-up approach: starts with defining specific classes

and proceeds with generalization Mixed

47Chapter 3


3. Check the ontology for syntactic, logical and semantic inconsistencies

4. Publish and deploy the ontology

5. Ontologies need to be maintained, updated and perhaps linked with other ontologies

48Chapter 3


Ontology Web Language

Used to describe the terminology of documents on the web OWL builds on the Resource Definition Framework (RDF) and

proposes a specific vocabulary that provides selected frame and description logic primitives to capture ontologies

49Chapter 3


Three sublanguages with increasing expressiveness: OWL Lite: mainly intended for class hierarchies and limited

constraints. Lowest complexity OWL DL (includes OWL Lite): intended when completeness and

decidability are important OWL Full (includes OWL DL): maximum expressivity, but no

computational guarantees

50Chapter 3


OWL example

An ontology describes classes and relations between classes Example

51Chapter 3


Cooperative problem-solving

Key questions: How to determine shared goals and common tasks How to decompose tasks, match subtasks to available agents and

synthesize the partial results How to reconcile different views and avoid unnecessary conflicts What should be the structure of the system: fixed, self-repairing

or self-organizing How to facilitate convergence on solutions despite incomplete or

inconsistent knowledge or data

The design of systems of agents that successfully cooperate to solve a difficult and large problem or perform a complex task

52Chapter 3


MAS for cooperative problem-solving

Complex Task

Task Decomposition

Sub-sub-sub tasks performed

53Chapter 3


Task decomposition

Can be programmed by the designer during implementation or by the agents using hierarchical planning

Can be done spatially based on the layout of information resources or decision points

Can be done functionally according to the expertise available to the agents

Given a complex task, how can this be divided into smaller subtasks?

54Chapter 3


Task distribution

Criteria for task distribution Avoid overloading critical resources Assign tasks to agents with appropriate capabilities Make an agent with a wide view assign tasks to other agents Assign overlapping responsibilities to agents to achieve

coherence Assign closely-related tasks to agents in spatial or semantic

proximity Reassign tasks if necessary for completing urgent tasks

Once a complex task has been decomposed the subtasks need to be distributed

55Chapter 3


Task distribution mechanisms

Market Mechanisms: tasks are matched to agents by generalised agreement

Multi-agent planning: planning agents have the responsibility for task assignment

Organizational structure: agents have fixed responsibilities for particular tasks

ContractNet

56Chapter 3


The ContractNet protocol

Developed by Reid Smith, it is an interaction protocol for co-operative problem-solving among agents

Modelled on the contracting mechanism used by businesses to govern the exchange of goods and services

Provides a solution to the connection problem: finding an appropriate agent to work on a given task

57Chapter 3


Manageragent

Contractoragent

Task announcementTask evaluation

Bid message

Task performed

Reports

Bid evaluation

Award message

Report assimilation

Termination message

Contract established

58Chapter 3


In the original ContractNet implementation each node (agent) comprised of

communication processor task processor contract processor local database

A common internode language with a very simple grammar and a partly domain-independent vocabulary was used for communication

59Chapter 3


Advantages of the ContractNet

Simple to implement, and constitutes the basis for other protocols Fully distributed Graceful performance degradation

Applications suitable for the ContractNet protocol are those that feature:

A hierarchy of tasks or levels of data abstraction Relatively large subtasks which justify considerable effort

being put into agent selection Examples: manufacturing control and scheduling

60Chapter 3


Limitations of the ContractNet

A manager may not receive any bids because: all potential contractors are busy with other tasks a potential contractor is idle but may rank the proposed task

below other tasks under consideration no contractors, even if idle, are capable of working on the task

(the manager may request immediate responses from contractors with messages such as eligible but busy, ineligible, or uninterested)

Managers are under no obligation to inform potential contractors that a contract has already been awarded

61Chapter 3


An agent may bid on a task for which it is marginally suited and preclude itself from bidding on a much more suitable task announced soon afterwards

Agents have knowledge only of contracts which they are part of. So an agent cannot decide its bids in the light of other existing bids and contracts

Agents do not know how busy other agents are. This is especially important for managers

Agents must know how to decompose tasks/problems and synthesize solutions out of sub-solutions

62Chapter 3


Virtual organizations as MASs

Organizations need to be agile and innovative to be able to withstand competition and respond to the changing needs of the customer force

Virtual organization (VO), virtual enterprise (VE) or agile enterprise: comprises a number of partners that forge a consortium dynamically. Reasons:

Meet customer demand Offer new or improved services or added value Exploit a niche in the market

VOs can be naturally seen as MASs Since agents are autonomous, integration and coordination are

fundamental issues

63Chapter 3


Lifecycle of a virtual organization

Creation One or more agents become aware of the need for a VO Potential partners are contacted and invited to join Agent selection is important as it determines future success

Operation Tasks are distributed to partners who also need to coordinate

their actions

64Chapter 3


Maintenance Essential in a dynamic environment as conditions change Partners may leave and have to be replaced Changes in the structure and objectives may be necessary Self-organization

Disbanding

Marks the end of the VO When the VO has served its purpose, or is no longer

profitable, the services provided are no longer needed or competitive

65Chapter 3


Infrastructure requirements for MASs

To facilitate the creation, operation and interaction of multiple agents an underlying infrastructure is needed

A MAS infrastructure consists of the services, conventions and knowledge and facilitate agent interactions

The MAS infrastructure views agents as black boxes – nothing is known about their internal workings

It is the responsibility of the agent developer to ensure that the agent has appropriate components that allow it to function and interact with other agents and use the services provided by the infrastructure

66Chapter 3


The set of services that are considered essential for the enactment and support of open MASs

Operating Environment

Communication Infrastructure

ACL Infrastructure

Performance and Management Services

Security and Trust Management Services

Agent Description and Discovery Services

Interoperation Services

MAS Infrastructure

Documents

Agent Technology for e-Commerce Chapter 3: Multi-agent Systems Maria Fasli