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Fariba Sadri - ICCL 08 In troduction 1 Agents Introduction Fariba Sadri Imperial College London ICCL Summer School Dresden August 2008
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Page 1: Fariba Sadri - ICCL 08 Introduction 1 Agents Introduction Fariba Sadri Imperial College London ICCL Summer School Dresden August 2008.

Fariba Sadri - ICCL 08 Introduction

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AgentsIntroduction

Fariba Sadri Imperial College London

ICCL Summer School DresdenAugust 2008

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Plan for the Course

• Introduction• Some agent examples

– TR programs– Agent0– AgentSpeak

• Abductive logic programming (ALP)• ALP for agents• An example of an ALP-based agent

model

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Introduction - Contents

• Agent definition• Why are agents useful• Some application areas• A classification of agents

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General Acknowledgements

In preparing these lectures I have used or been inspired by material from

Keith Clark, Bob Kowalski, and many online sources.

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What is an Agent?

sensors

environmentagent

?

actuators

percepts

actions

An intelligent agent perceives its environment via sensors and acts rationally upon that environment with its actuators.

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What is an Agent?

Agent SYSTEM

ENVIRONMET

input output

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Agent: Definition

Many definitions by different authors, but they have much in common:

• An agent is a computer system that is capable of exhibiting some form of intelligence and independent action on behalf of its user or owner.

• Another agent definition from:Is it an Agent, or just a Program?: A Taxonomy forAutonomous Agents, Franklin and Graesser http://www.msci.memphis.edu/~franklin/AgentProg.html

An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future.

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Agent: Definition cntd.

• Another definition from AgentLink Agent Roadmap Definition :

An agent is a computer system that is capable of flexible autonomous action in dynamic, unpredictable, typically multi-agent domains.

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Agent DefinitionKey Properties

An agent is a hardware or software system that is:• Situated:

i.e. embedded in some environment (which may be the physical world, a software environment, a community of agents) which they can:– sense (through physical sensors or message receipt or

event detection giving partial info on environment state) and

– act upon (via effectors, messages or event generation with possible non-deterministic outcomes)

• Reactive :i.e. responds in a timely fashion to messages, sensed data or detected events - so actively monitors state of its environment

• Autonomous:i.e. operates without the direct intervention of humans or other agents, with independent control over its actions and internal state

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Agent Definition Other Possible properties

• Social: can interact with other agents and possibly humans using messages or actions that change the shared environment

• Pro-active: has one or more goals which it tries to achieve by communicating with other agents or acting on its environment

• Has a mentalistic model: agent has an internal architecture that can be understood in terms of mentalistic notions such as beliefs, desires, intentions and obligations

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E.g. Humans Situated Sensors:

Eyes (vision), ears (hearing), skin (touch), tongue (taste), nose (olfaction), neuromuscular system (proprioception)

Percepts: At the lowest level – electrical signals After preprocessing – objects in the visual field (location,

textures, colors, …), auditory streams (pitch, loudness, direction), …

Actuators: limbs, digits, eyes, tongue, … Actions: lift a finger, turn left, walk, run, carry an

object (Often) Intelligent and Autonomous

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E.g. Artificial Agents

Agent Environ-ment

Goal Percepts

Action

Financial Forecaster

Stock market

Maximise investments

Stock market data

Pick stocks to buy/sell

Medical Diagnostic

Patient, Hospital

Patient Care

Symptom, Test results

Tests, Treatments

Deep Blue Chess board, opponent

Win Current board state

Choose next move

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Why are Agents useful?

• Specialised tasks Agents (and their physical instantiation in robots) have a role to play in high-risk situations, unsuitable or impossible for humans

• In applications where the data, control or resources are distributed:The system can be conceptualised as a collection of co-operating components

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Why are Agents useful?

• Agents as a tool for understanding human societies:Multiagent systems provide a novel new tool for simulating societies, which may help shed some light on various kinds of social processes.

• Agents as tools for formalising and experimenting with theories of cognition

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For example:Sloman A., Architectural requirements for

human-like agents both natural and artificial (What sort of machines can love?). In K. Dautenhahn (ed.) Human cognition and social agent technology. Advances in consciousness research, 2000, 163-195.

Sloman A. & Logan B., Building cognitively rich agents using the Sim_Agent toolkit. Communivcations of the ACM, 1999, 42(3), 71-77.

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Why are Agents useful?

• Agents as a paradigm for software engineering:– Increasing complex software– It is now widely recognized that

independence of components and their interaction are very important characteristics of complex software

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Some Application Areas

• Computer games(http://www.ai-junkie.com/books/toc_pgaibe.html)

• Work flow and business process management(http://www.eil.utoronto.ca/iscm-descr.html)

• Simulation– Social, economic, behavioural

(http://jasss.soc.surrey.ac.uk/5/1/7.html)– Complex systems

(http://www.jot.fm/issues/issue_2002_07/column3)

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Some Application Areas• Ambient intelligence

(http://research.nii.ac.jp/~ichiro/papers/satoh-smc2004.pdf)

Examples: MavHome (Managing An Intelligent Versatile Home) project at the

University of Texas at Arlington: objective to create a home that acts as a rational agent, that has sensors and effectors, and that acquires and applies information about the inhabitants to provide comfort and efficiency

iDorm (intelligent dormitory) at the University of Essex, UK. The iDorm contains space for various activities such as sleeping, working and entertaining, and contains various items of furniture such as a bed, desk, wardrobe, and multimedia entertainment system. It is fitted with multiple sensors and effectors. The sensors can sense temperature, occupancy (for example user sitting at desk, user lying in bed), humidity, and light levels. The effectors can open and close doors, and adjust heaters and blinds.

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Some Application Areas Ambient intelligence

• Much has been done on hardware• Much more required on “intelligence”• Lends itself well to agents and many areas

of AI• Distributed information via sensors• Distributed information about user profile• Partial information• Defeasible reasoning

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Some Application Areas

• E-commerce• Information gathering and retrieval

(http://www.doc.ic.ac.uk/~klc/iceis03.html)

• Semantic web(http://citeseer.ist.psu.edu/

hendler01agents.html)

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INTELLIGENT AGENTSA Classification

Adopted from Russell and Norvig’s book, Artificial Intelligence A Modern Approach:

1. simple reflex agents2. model-based reflex agents3. goal-based agents 4. utility-based agents

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1. Simple reflex agents

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With perception

Environment

Agent

see action

Simple reflex agents

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Example: Simple reflex agents

Percept ActionAt A, A Dirty VacuumAt A, A Clean Move LeftAt B, B Dirty VacuumAt B, B Clean Move right

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Simple reflex agents

Act only on the basis of the current percept. The agent function is based on the condition-action rule: condition

action

Limited functionality: Work well only when • the environment is fully observable and • the condition-action rules have predicted

all necessary actions.

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2. Model-based reflex agents

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Model-based reflex agents• Have information about how the world behaves – Model of

the World.• They can work out information about the part of the world

which they have not seen.• Handle partially observable environments.

The model of the world allows them to– Use information about how the world evolves to keep track of

the parts of the world they cannot see• Example: If the agent has seen an object in a place and has since

not seen any agent moving towards that object then the object is still at that place.

– Know the effects of their own actions on the world.• Example: if the agent has moved northwards for 5 minutes then it

is 5 minutes north of where it was.

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Model-based reflex agents

With internal states

Environment

Agent

see action

statePredict

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Model-based reflex agents

Given a Percept Integrate Percept in State ==> StateEvaluate the condition-action rules in

State and choose ActionExecute ActionUpdate State with Action ==> State

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3. Goal-based agents

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Goal-based agents

Environment

Agent

see action

Predict state

DecisionGoals

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Goal-based agents

• The current state of the world is not always enough to decide what to do.

• For example at a junction a car can go left, right or straight. It needs knowledge of its destination to make the decision which of these to choose.

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Goal-based agents

World Model (as model-based agents) + Goals

Goals are situations that are desirable.The goals allow the agent a way to

choose among multiple possibilities, selecting the one which reaches a goal state.

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Goal-based agents

Differences from Reflexive Agents:• Goals are explicit• The future is taken into account• Reasoning about the future is

necessary – planning, search.

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4. Utility-based agents

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Utility-based agents

• What if there are multiple alternative ways of achieving the same goal?

• Goals provide coarse distinction between “happy” and “unhappy” states.

• Utility-based agents have finer degrees of comparison between states.

• World Model + Goals + utility functions

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Utility-based agents

Utility functions map states to a measure of the utility of the states, often real numbers.

They are used to:• Select between conflicting goals• Select between alternative ways of

achieving a goal• Deal with cases of multiple goals, none

of which can be achieved with certainty – weighing up likelihood of success against importance of goal.

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Multi-Agent Systems

Agent

Agent

Agent

Agent

Agent

Agent

ENVIRONMENT

ENVIRONMENT

ENVIRONMENT

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Multiagent SystemsFeatures

• Interaction – Communication languages– Protocols– Policies

• Co-ordination• Co-operation• Collaboration : Shared goals• Negotiation


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