Agents Introduction

1
AgentsIntroduction

• Fariba Sadri
• Imperial College London
• ICCL Summer School Dresden
• August 2008

2
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

3
Introduction - Contents

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

4
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?
An intelligent agent perceives its environment
via sensors and acts rationally upon that
environment with its actuators.
6
What is an Agent?
Agent SYSTEM
output
input
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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.ht
  ml
• 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
  societiesMultiagent 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-sm
  c2004.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 Norvigs book,
  Artificial Intelligence A Modern Approach
• simple reflex agents
• model-based reflex agents
• goal-based agents
• utility-based agents

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1. Simple reflex agents
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Simple reflex agents

• With perception

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• Example Simple reflex agents
• Percept Action
• At A, A Dirty Vacuum
• At A, A Clean Move Left
• At B, B Dirty Vacuum
• At 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

Agent
see
action
state
Predict
Environment
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Model-based reflex agents

• Given a Percept
• Integrate Percept in State gt State
• Evaluate the condition-action rules in State and
  choose Action
• Execute Action
• Update State with Action gt State

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3. Goal-based agents
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Goal-based agents
Agent
Decision
Goals
see
action
Predict
state
Environment
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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
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Multiagent SystemsFeatures

• Interaction
• Communication languages
• Protocols
• Policies
• Co-ordination
• Co-operation
• Collaboration Shared goals
• Negotiation