abc introduction group

8/11/2019 ABC Introduction group

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peer-to-peer andagent-based computing

Agents & Multi-AgentSystems: Introduction


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peer-to-peer and agent-based computing 2

Agents and Multi-agent Systems (MAS) Software agents are programs with unique features

such as: Autonomy (no/little human input or interference) Proactiveness (act in an anticipatory fashion, i.e., before

things happen) Social skills (agents communicate)

A MAS has many autonomous entities (the agents )interacting with one other to achieve Conflicting goals ( competition ) or

Joint goals ( cooperation )Inevitable conclusions: not every program is a software agent not every IT/e-business solution requires a MAS


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Agents, MASs & AI Classical Artificial Intelligence (AI) concentrates on

modelling single agents: A single , stand-alone expert system A single , stand-alone natural language processing

system A single , stand-alone robot that learns ...

Work on agents & MASs investigate scenarios inwhich various agents communicate:

Individual agents do not have to be clever The intelligence may only arise in the agents

collective behaviour , as in, for instance, an ant colony


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The goal of this part of the course is Introduce aspects of agents and MAS:

1. Case studies and technologies (platforms,languages)2. Communication languages and protocols3. Auctions and voting mechanisms4. Negotiation5. Game theory6. Mobile agents

7. Sensor networks


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Agent-Oriented Programming Computer science has a number of goals:

Develop human-oriented, ubiquitous systems, Interconnect disparate elements, Create systems to make autonomous and intelligent

decisions about delegated tasks.

Programming has progressed through: sub-routines, procedures and functions, abstract data types, and objects to agents


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Agents in e-CommerceSoftware agents can be delegated tasks such as:

Information retrieval Finding the cheapest/best supplier Maintaining a companys stock of components Buying and selling in an energy market Management & coordination of business processes


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Agents in e-ScienceSoftware agents can be delegated tasks such as:

Finding an appropriate computational resource Distributing results of scientific experiments Negotiating the provision of a high-demand tool Management & coordination of scientific processes



How to build agents & MASs How do we build software agents to carry out the

tasks delegated to them? How do we build agents that can interact with others in

order to do what they were told to We may not have control as to how the other agents

out there will behave!

How do we engineer a MAS consisting of manyindependent components that interact? How do we address features from individual agents as

well as global properties of the group of agents? Do we write each agent individually? There may be

100s of them! Lets make this discussion more concrete:

Scenario: agentifying Microsofts Outlook



How to build agents & MASs (Contd) Microsofts Outlook offers means for people to share their

diaries: A person can organise a meeting and invite other people to

attend it An invitation can either be accepted or refused

If refused the person who is trying to organise the meeting willneed to try again, and tell everyone who has accepted that themeeting will be changed, etc.

This is a tedious process which can take a long time! Would it not be better if the people who could not attend

suggested a time that would suit them? This task could, however, be delegated to a program!

How would you program this?



How to build agents & MASs (Contd)

Can we meet at 2PM tomorrow?

Sorry, I cant. What about 3PM? Failingthat, would Monday be any good?

We would want the following to happen:

Dead easy: A program provides communication between running copies of Outlook

Which language? This program exchanges messages

Which kinds of messages are permitted and in which order?

Can we ensure the dialogue always terminates? Which setup? Peer-to-peer? Client-server? Will we allow foreign agents to join in?



How to build agents & MASs (Contd) However, the solution should scale up :



The Agent Concept What is an agent?

Agent characteristics Agents and other things Environment characteristics The intentional stance



What is an Agent? Main point:

Agents act independently , controlling their internal state Agents are capable of autonomous action in some

environment within which they live Agents actions may affect/change the environment

Environment, on its turn, is the input to the agent


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The EnvironmentEnvironments are:

The physical or virtual space within which the agentworks Examples:

A program controlling a door mechanism

A program roaming the Web for cheap flights to Dublin


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If the environment is fixed and Only the agent can cause the environment to change The effects of the actions are stable

then the system may execute blindly. E.g., a thermostat and a heater

However, realistic environments are not like that: Other factors influence the environment The effects of actions may be unstable E.g., agents in a network of computers

Interesting environments are dynamic

Agents: reactivity


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Software for dynamic environments is hard to build! It must expect the unexpected & possibility of failure

Reactive system: Maintains an on-going interaction with its environment Responds to significant changes in time for the response to be

useful (no point in responding 3 hours later)

Given a stimulus , agents respond with a designatedresponse: Programming by events outwith the agents control If the environment is very diverse then we cannot foresee all

possible events Therein lies the challenge and beauty of agent design

Agents: reactivity (Contd)


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Agents monitor and react to environment

On top of that, we want our agents to achieve thegoals we delegated to them: E.g., a shop-bot should text us with a deal on tickets

This feature of agents is called proactiveness :

When left on its own, an agent that has programmedgoals, will strive to achieve them It is part of its design to actively pursue its goals

Agents: goal-directed behaviour


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A lonely agent is a contradiction : Many agents can achieve something bigger than each of them

can do in isolation The world is a large multi-agent environment:

People, Java programs, Web services, etc. The actions and goals of other agents must be taken into account

when we are designing our agents Some goals can only be achieved with the cooperation of other

agents; other goals may lead to competition ! Agents should be endowed with social abilities :

The ability to interact with other agents meaningfully Direct or indirect communication (message-passing) Terms of a cooperation can be negotiated and agreed

Agents: social ability


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Agents may also possess Mobility : the ability to move around machines Veracity : they will not knowingly provide false

information Benevolence : they will, if possible, do what is asked Rationality : an agent will act towards achieving its goals,

and not act to make these goals unachievable Learning / adaptation skills : agents improve their

performance from experience

Agents: other features


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Reading List An Introduction to Multi-Agent Systems , M.

Wooldridge, John Wiley & Sons, 2008

Is it an agent or just a program? S. Franklin and A.Graesser, available on-line athttp://www.msci.memphis.edu/~franklin/AgentProg.html

Artificial Intelligence: A Modern Approach (2nd Ed.),S. Russell and P. Norvig, Prentice-Hall, 2003.

Multi-Agent Systems: A Modern Approach toDistributed Artificial Intelligence , G. Weiss (editor),MIT Press, 1999.

Multi-Agent Systems: An Introduction toDistributed Artificial Intelligence , J. Ferber, Addison-Wesley, 1999.

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