Design of multi agent systems
Download
1 / 41

Design of Multi-Agent Systems - PowerPoint PPT Presentation


  • 149 Views
  • Uploaded on

Design of Multi-Agent Systems. Teacher Bart Verheij Student assistants Albert Hankel Elske van der Vaart Web site http://www.ai.rug.nl/~verheij/teaching/dmas/ (Nestor contains a link). Overview. Agents, multi-agent systems This course Views of the field Objections

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Design of Multi-Agent Systems' - caesar


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Design of multi agent systems
Design of Multi-Agent Systems

  • Teacher

  • Bart Verheij

  • Student assistants

  • Albert Hankel

  • Elske van der Vaart

  • Web site

  • http://www.ai.rug.nl/~verheij/teaching/dmas/

  • (Nestor contains a link)


Overview
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


Russell norvig
Russell & Norvig

  • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.


Michael wooldridge
Michael Wooldridge

  • An agent is a computer system that is situated in some environment and that is capable of autonomous actions in order to meet its design objectives.


Some agents
Some agents

  • Mars Path Finder

  • Air traffic control

  • Personal digital assistant

  • P2p file sharing

  • Game agents


A natural kinds taxonomy of agent
A natural kinds taxonomy of agent

  • (Franklin and Graesser)


Multi agent systems
Multi-agent systems

  • A multi-agent system is one that consists of a number of agents, which interact with one-another

  • This requires the ability to cooperate, coordinate, and negotiate with each other


Multi agent systems1
Multi-agent systems

  • How can cooperation emerge in societies of self-interested agents?

  • What kinds of languages can agents use to communicate?

  • How can self-interested agents recognize conflict, and how can they (nevertheless) reach agreement?

  • How can autonomous agents coordinate their activities so as to cooperatively achieve goals?


Reactivity
Reactivity

  • A reactive system is one that maintains an ongoing interaction with its environment, and responds to changes that occur in it (in time for the response to be useful)


Proactiveness
Proactiveness

  • A proactive system is one that generates goals and attempts to achieve them by taking initiatives and recognizing opportunities


Balancing reactive and goal oriented behavior
Balancing reactive and goal-oriented behavior

  • Timely response

  • to changing conditions

  • Systematically working

  • towards long-term goals


Influences and inspiration
Influences and inspiration

  • Economics

  • Philosophy

  • Game Theory

  • Logic

  • Ecology

  • Social Sciences


Overview1
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


This course examination
This course: examination

  • 50% Exam about the course book

  • Wooldridge’s An Introduction to Multiagent Systems

  • chs 1-4, 6-11

  • 25% Programming exercises

  • To be submitted to [email protected] using certain naming conventions

  • 25% A presentation



This course time investment
This course: time investment

  • 5 ECTS = 140 hours

  • 140 hours/10 weeks = 14 hours per week

    6 contact hours (2 hours lecture, 2 hours presentations, 2 hours computer lab)

    4 hours self study

    2 hours presentation (10 hours of study, 4 uur slide design / 7 weeks)

    2 hours programming (so 4 programming hours per week when the lab session is included)


Overview2
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


Some views of the field
Some views of the Field

  • Multi-agent systems as a paradigm for software engineering

  • Interaction is probably the most important single characteristic of complex software

  • Multi-agent systems as a tool for understanding human societiesSocial simulation, “theories of the mind”

  • Multi-agent systems as a search for appropriate theoretical foundations“Neat” vs “scruffy”; theory vs engineering


Overview3
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


Objections to mas
Objections to MAS

  • Isn’t it all just distributed/concurrent systems?

    • Agents can be self-interested, so their interactions are “economic” encounters. There is no global goal.

  • Isn’t it all just AI?

    • Agents may not need much intelligence

    • Classical AI ignored social aspects of agency.


Objections to mas1
Objections to MAS

  • Isn’t it all just economics/game theory?

    • These fields ignored computational constraints and resource-bounded decision making

  • Isn’t it all just social science?

    • Actual societies may not be optimal


Overview4
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


Agents and objects
Agents and objects

  • Are agents just objects by another name?

  • Object:

    • encapsulates some state

    • communicates via message passing

    • has methods, corresponding to operations that may be performed on this state


Agents and objects1
Agents and objects

  • Main differences:

    • Agents are autonomous:they decide for themselves whether or not to perform an action on request from another agent

    • Agents are smart:they are capable of flexible (reactive, pro-active, social) behavior, and the standard object model has nothing to say about such types of behavior

    • Agents are active:a multi-agent system is inherently multi-threaded, in that each agent is assumed to have at least one thread of active control


Objects do it for free
Objects do it for free…

  • Agents do it because they want to

  • Agents do it for money


Overview5
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


Agents as intentional systems
Agents as intentional systems

  • Dennett: an intentional system is an entity ‘whose behavior can be predicted by the method of attributing belief, desires and rational acumen’

  • McCarthy: ‘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’


Agents as intentional systems1
Agents as intentional systems

  • For more complex systems, we need more powerful abstractions and metaphors to explain their operation — low level explanations become impractical.

  • The intentional stance is such an abstraction.


Overview6
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


Abstract architecture for agents
Abstract architecture for agents

  • Environment states:

  • Actions of agents:

  • A run:


Agents environments systems
Agents, environments, systems

  • An environment is a triple Env =E,e0, where: Eis a set of environment states, e0 E is the initial state, and  is a state transformer function

  • An agent is a function which maps runs to actions:

  • A system is a pair Env, Ag.


Some variations
Some variations

  • A deterministic environment:

  • A reactive agent:

  • Ag:E → Ac

  • A state transition function that is independent of history:

  • T : EAc(E)


Agents with perception
Agents with perception

see

action

Agent

Environment


Agents with internal states
Agents with internal states

Agent

see

action

state

next

Environment


Agents with tasks
Agents with tasks

  • Utility functions can be used to tell an agent what to do without telling how to do it

  • The task of the agent is to bring about states that maximize utility



Optimal agents
Optimal agents

  • P(r | Ag, Env) denotes the probability of run r for agent Ag and environment Env

  • An agent is optimal when it maximizes expected utility


Task specification using predicates
Task specification using predicates

  • Predicates Ψ: R → {0, 1} can be used for task specification:

  • Ψ(r) = 1 expresses that an agent has succeeded, Ψ(r) = 0 that that it has not.

  • An agent succeeds in a task environment (Env, Ψ) when


Types of tasks
Types of tasks

  • Achievement tasks

  • Achieve a state of affairs

    Reach a state in a set of goal states G:

    Ψ(r) if and only if r contains a state in G

  • Maintenance tasks

  • Maintain a state of affairs

    Avoid a set of failure states B:

    Ψ(r) if and only if r does not contain a state in B


Overview7
Overview

  • Agents, multi-agent systems

  • This course

  • Views of the field

  • Objections

  • Agents & objects

  • Intentional systems

  • Abstract architecture


ad