Agents and Multiagent Systems Chapter 6 Dr Ahmed Rafea Transition - - PowerPoint PPT Presentation

Agents and Multiagent Systems

Chapter 6 Dr Ahmed Rafea

Transition from AI to IA

• There are many alternative artificial

intelligence techniques for knowledge representation , reasoning and learning.

• The specific functions and requirements of

an intelligent agent are the prime determinant of which AI technique should be used.

Knowledge representation

• Knowledge representation is a crucial

issue.

• What our agent is expected to do and in

what domain, will have a significant impact

• n the type of knowledge representation

we should use.

Reasoning

• The amount of intelligence required by an

agent , in terms of the size of the knowledge base and sophistication of the reasoning algorithms , is significantly impacted by the degree of autonomy and mobility the agent has .

• Mobile agents place special requirements
• n the security of the knowledge base it

travels through the network.

Learning

• Whether learning is a desirable function

depends on the domain the intelligent agent will work in , as well as the environment.

• Learning is most useful when an agent is

used in complex environments to perform repetitive tasks , or when the agent must adapt to unknown situations.

Autonomous Intelligent Agents

Requirements for Autonomous Intelligent Agents include:

• Perception
• Taking Action

Perception

• In order for a software agent to take some

intelligent action , it first has to be able to perceive what is going on around it.

• An intelligent agent uses its sensors as a

source of information.

• A fundamental part of perception is the

ability to recognize and filter out the expected events and attend to the unexpected ones.

Taking Action

• Intelligent Agents use effectors to take actions

either by sending messages to other agents or by calling application programming interfaces or system services directly.

• If our agent takes an action directly under its

control, we can probably consider it done. However, when we are dealing with intermediaries , whether other agent or unknown systems, then some extra precautions and checking are probably in order.

Multiagent Systems

• Multiagent systems are applications in

which many autonomous software agents are combined together to solve large problems.

• The RoboCup challenge is an example of

the current state-of-the-art of multivalent systems , in which teams of autonomous agents compete in a simulated soccer tournament.

Blackboards

• Blackboard is the oldest multiagent system

architecture used as a problem-solving technique.

• The Blackboard is a data structure that is

used as the general communication mechanism for the multiple knowledge sources and is managed and arbitrated by a controller.

Blackboards

• As each agent works on its part of the

problem, it looks to the blackboard to pick up new information posted by other agents , and it, in turn, posts its results to the Blackboard.

• Blackboard systems are used as a

communication mechanism when building single large applications and want to modularize the knowledge bases.

Communication

• An environment where agents with very

different structures and with no knowledge

• f a centralized background can work

together, the agents will need to communicate with each other.

• Communication can be :
• Directly to each other
• Through an interpreter or facilitator.

Communication

• To be able to communicate, a language is

needed.

• There is a level of basic language which is the

syntax and format of the messages and there is a deeper level, the meaning or semantics.

• For the semantics to be easily understood , a

shared vocabulary of words and their meanings is needed. This shared vocabulary is called an

• ntology.
• The most widely used agent communication

language (ACL) is Knowledge Query and Manipulation Language (KQML).

Knowledge Query and Manipulation Language

• Knowledge Query and Manipulation Language (KQML) provides a

framework for a set of independent agents to communicate and cooperate

• n a problem using messages called per formatives.

– Directives: commands or requests – Representatives: facts or beliefs – Commissives: promises or threats

• KQML uses ontologies to ensure that two agents are communicating in the

same language

• KQML messages encode information at three different architectural levels:

content, message and communication. An example of a KQML message from agent joe asking about the price of a share of SUN stock might be encoded as:

(ask-one :sender joe

• -comm. level

:content (real price = sun.price())

• -content level

:receiver stock-server

• -comm. level

:reply-with sun-stock

• -comm. level

:language java

• -message level

:ontology NYSE-TICKS)

• -message level

Agent Standards

• Standards are becoming more important

as agents become a large part of the electronic commerce infrastructure.

• Two major efforts of standardization are:

– The Foundation for Intelligent physical Agents (FIPA) that is focused primarily on agent-level issues. – The Object Management Group (OMG) that is focused on object-level interoperability and management

FIPA & OMG

• FIPA is dominated by computer and

telecommunications companies and is focused primarily on agent-level issues.

• OMG is the standards body that created

the Common Object Request Broker Architecture (CORBA) distributed object protocol and tends to focus on object-level interoperability and management.

Co-operating Agents

• Co-operation among agents allows a community
• f specialized agents to pool their capabilities to

solve large problems but with the additional cost

• f communication overhead.
• Distributed systems management, electronic

commerce and multi agent design systems are three application areas in which co-operating agents have been applied.

• It is likely that a combination using the team

structure and roles to limit communications , along with distributed planning techniques , will provide the best solution to building multiagent teams.

Competing Agents

• Competition between agents will occur as soon

as intelligent agents are deployed by individuals

• r companies with different agendas and those

agents interact in the e-commerce environment

• Intelligent agents will be used to provide

advantages for individuals and businesses.

• Negotiation protocols, such as Contract Net ,

auctions and bargaining, are used to allow agents to compete for business.

Agent Software Engineering Issues

• Designing multiagent systems is similar to
• bject-oriented but requires some additional

analysis and modeling techniques.

• A common approach for designing agents and

multiagent systems is to define roles for team members.

• While agent applications are becoming

increasingly popular, there have not been many proposals for agent-oriented methodologies for analysis, design, and software development.

Designing Agents

Two Popular methods are:

• The agent modeling technique for systems of
• agents. This approach looks to the problem from

two perspectives: an external and internal one.

– External: The agents themselves (agent Model) and their interactions (interaction model) – Internal: Relationship with other agents, a goal, and a plan to achieve the goal

• CoMoMas extension to the CommonKADS

knowledge engineering methodology.

Multi Agent Interaction Models

• Model 1:

Requesting Agent Facilitator Service Agent

1-Request 2-Request 3-Response 4-Response

Multi Agent Interaction Models

• Model 2

Requesting Agent Facilitator Service Agent

1-Request 2-Request 3-Response

Multi Agent Interaction Models

• Model 3

Requesting Agent Facilitator Service Agent

1-Request 2- Address of service Agent 3-Request 4-Response