peer-to-peer and agent-based computing agents & multi-agent systems: introduction (contd)

peer-to-peer and agent-based computing

Agents & Multi-Agent Systems: Introduction (Cont’d)

peer-to-peer and agent-based computing 2

Objection: agents are just objects!• Objects:

– Encapsulate state– Communicate via message-passing– Have methods corresponding to operations performed

on its state

Agents: some objections


Agents: some objections (Cont’d)Agents, however, have unique characteristics:

– They embody a stronger notion of autonomy than objects

– They decide for themselves how to respond to a message

– They are capable of flexible behaviour • reactive,• goal-directed• social

The standard object model has nothing to say about these types of behaviour

– They are active• A MAS is inherently multi-threaded – each agent is assumed to

have at least one thread of control


Agents vs. objects• Agents initiate actions on their own, objects don’t• Agents are aware of their environment, objects

aren’t• If requested,

– Objects do it because they have to,– Agents do it if they decide to…

Agents: some objections (Cont’d)


Agents: some objections (Cont’d)• Objection: agents are just a re-branding of expert

systems.• Expert systems encapsulate disembodied expertise

about some domain• Agents are:

– Situated in an environment, and– Act on that environment.

• However, some real-time (e.g. process control) expert systems are agents…


Is it all AI, then?• Agents have enjoyed a close relationship with AI,

but they are different (intersecting) fields• Distributed AI:

– Intelligence emerging from teams of experts– Distributed problem-solving

• Agents embody and integrate different kinds of intelligence:

– Planning, reasoning, search, learning, etc.• No need to solve all of AI to program agents:

– A little intelligence goes a long way…

Agents: some objections (Cont’d)


Environments can be:• Accessible

– agents can obtain complete, accurate and up-to-date information about the state of the environment.

– The more accessible an environment is, the simpler it is to build agents to operate in it.

– As an environment becomes more complex, it becomes less accessible

• Deterministic– Actions have guaranteed and predictable effects.– In other words, there is no uncertainty about the state of the

world that will result from performing an action.– Non-deterministic environments pose greater challenge to

agents’ design– The physical world can be regarded as non-deterministic (even if

we assume that the physics of the environment can be reliably modeled).

Characteristics of environments


Environments can be:• Static:

– Change only due to the agent’s actions and the effects of the actions are stable.

• Dynamic: – Other agents’ actions or entropy may change it.

• Discrete:– There are a fixed and finite number of possible states.

• Continuous:– There are infinite possible states.– A chess game is a discrete environment and taxi

driving is a continuous environment.

Environments (Cont’d)


• Agents are systems/programs that– Perceive their environment through sensors– Act upon the environment through actuators

– Sensors:• Cameras, keystrokes, streams arriving in sockets, etc.

– Actuators:• Variable, web page, database entry, robot arm, etc.

Inside agents

Enviro

nment

Agent Sensors

Actuators

?

Percepts

Actions


• Percept:– The agent’s perceptual inputs at a given instant– What the agent makes of the environment– Percept sequence is the history of what the agent has

perceived so far.– An agent’s choice of action can depend on the entire

percept sequence observed to date.• An agent function uniquely defines the behaviour:

– We can provide the same effect with a program!

Inside agents (Cont’d)

f(p1,…,pn) = action1

f(q1,…,qm) = action2

…


• Simple reflex agent– Takes action based on current percept

Agent programs

Enviro

nment

Percepts

Actions

Agent Sensors

Actuators

What the world is like now

What action I should do now

Condition-action rules


• Model-based reflex agent– Uses model of the world to make up for incomplete info

Agent programs (Cont’d)

Enviro

nment

Percepts

Actions

Agent Sensors

Actuators



Condition-action rules

State

How the world evolves

What my actions do


• Model-based utility-based agent– Uses utility function to choose among possible actions

Agent programs (Cont’d)Enviro

nment

Percepts

Actions

Agent Sensors

Actuators



Utility

State

How the world evolves

What my actions do What it will belike if I do action A

How happy I willbe in such a state


The Intentional Stance• Daniel Dennett coined the term intentional system

to describe entities:– “whose behaviour can be predicted by the method of

attributing belief, desires and rational acumen”• When explaining human behaviour, it is

useful to make statements such as:– John enrolled in the course because he

believed that a degree would help him get a better job

– Sue worked hard because she wanted to graduate

Daniel Dennett

http://en.wikipedia.org/wiki/File:Daniel_Dennett_in_Venice_2006.png


Intentions for Machines? “To ascribe beliefs, free will, intentions, consciousness, abilities or wants to a machine is

legitimate when such an ascription expresses the same information about the machine that it expresses about a person

It is useful when the ascription helps us to understand the structure of the machine, its past or future behaviour, or how to repair or improve it.

It is perhaps never logically required even for humans, but expressing reasonably briefly what is actually known about the state of the machine in a particular situation may require mental qualities or qualities isomorphic to them.

Theories of belief, knowledge and wanting can be constructed

for machines in a simpler setting than for humans. Ascription of mental qualities is most straightforward for machines of known structure such as thermostats and computer operating systems, but is most useful when applied to entities whose structure is incompletely known.”

John McCarthy


The Intentional Stance (cont.)• What objects can be described by the intentional

stance? “It is perfectly coherent to treat a light switch as a (very

co-operative) agent with the capability of transmitting current at will, who invariably transmits current when it believes that we want it transmitted and not otherwise; flicking the switch is simply our way of communicating our desires” — Yoav Shoham

• This is absurd, but why?“… it does not buy us anything, as we understand the mechanism sufficiently to have a simpler, mechanistic description of its behaviour.”


Intentional Stance as an Abstraction• A mechanistic explanation of the behaviour of a

very complex system may not be practical• As computer systems become more complex, we

need more powerful abstractions and metaphors to explain their behaviour

• In computing, abstractions are important:– The procedural abstraction– Abstract data types– Objects– Agents as intentional systems


Intentional Stance: Advantages• It provides us with a familiar, non-technical way of

understanding and explaining agents without the need to understand the underlying mechanism.

• It gives us the potential to specify systems that include representations of other systems.

• It allows us to give a very abstract specification and leave it to the control mechanism, based on a theory of agency, to determine what to do.


Intentional Stance: Limitations• When modelling communication:

– If Bob says “Computers are the root of all evil”– What can we say about Bob’s psychological state as a

result of him saying this?– Can we say Bob believes it? Can we say that we believe

that Bob believes it?• When modelling organisations:

– Compare “every lecturer intends to attend the staff meeting”

with “every lecturer ought to attend the staff meeting”


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 to Distributed Artificial Intelligence, G. Weiss (editor), MIT Press, 1999

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

peer-to-peer and agent-based computing agents & multi-agent systems: introduction (contd)

Documents

agentbased computing

agents actions

objects agents

state agents

agent function

accessible agents

program agents

problemsolving agents