multiagent systems – an introduction

Ludwig-Maximilians-Universität München PST Lehrstuhl | PROF. DR. WIRSING

PROSEMINAR ADAPTIVE AGENTEN, SoSe 2012Betreuer: Dipl.-Inf. Christian Kroiß

Referentin: Huyen Linh Nguyen Vo10. Mai 2012

MULTIAGENT SYSTEMS –

AN INTRODUCTION

● Multiagent Systems are based on autonomous, intelligent agents:

– Autonomy – Reactivity– Proactivity– Social Ability

● Individual agent → systems of agents (sharing environment)

● ability to communicate with each other is given (same 'language', ability to understand each other)

10.Mai 2012 2

Basics

LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo

MOTIVATION

Motivation

10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 4

Abb.1: RoboCup

Abb.2: Soccerbots

● about four agents per team● each robot to be seen as an autonomous, intelligent agent

● analogy to 'real' soccer game

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Motivation

LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 10.Mai 201210.Mai 2012

● Central Questions:

– How does cooperation and coordination between agents, with a common goal function?

→ playing in a team– What happens when agents do not share the same

objectives, or are in competition with each other?

→ self-interest of agents– What kind of techniques are used by those agents

to come to a decision?

→ making group decisions

● Typical Structure of a Multiagent System● Working Together

– Cooperative Distributed Problem Solving

– Task Sharing (Contract Net)

– Result Sharing

– Coordination

● Making Decisions – Multiagent Interactions

● Preferences and Utilities● Techniques to find choices (Nash Equilibria)

– Making Group Decisions ● Voting Procedures● Auctions

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Agenda

10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo

STRUCTURE OF A MULTIAGENT SYSTEM

● common situation: agents in organisational relationship

● each agent has a 'sphere of influence' → overlapping included

● individual agent: take the other agents' actions into consideration → interactions

● agents don't always share common goals


Structure of a Multiagent System

Abb.3: Typical structure of a multiagent system, Wooldrige, 2009

WORKING TOGETHER

Working Together: CDPS

● Cooperative Distributed Problem Solving:

– studies how agents work together to solve problems beyond their individual capabilities or to increase efficiency

→ cooperation in order to solve problems– assumptions:

● all agents share the same goal

→ no conflict possibility● overall system objectives is all that matters● agents normally 'owned' by one organization

– CDPS to be viewed as a three-stage-activity


Working Together: CDPS


Abb.4: The three stages of CDPS, Wooldrige, 2009

Working Together: CDPS ● 1. problem decomposition (task sharing)

– dividing the problem into small subproblems

– each stage = further simplification of main problem

– condition: awareness of the abilities of each agent● 2. subproblem solution (result sharing)

– each agent individually solves given problem

– stage involves information sharing between agents● 3. solution synthesis

– integration of subsolutions to overall solution


Task Sharing:

● How are tasks allocated to individual agents?

● capability: homogeneous agents → any agent can do any task

● usually: different capabilities

● real autonomy → techniques to reach agreements (auctions, votings)


Working Together: Task Sharing

Abb.5: The Contract Net protocol (CNET) for Task Allocation, Wooldrige, 2009

● Task Sharing in the Contract Net:

– CNET: high level protocol: achieve efficient cooperation through task sharing in networks of communicating problem solvers (agents)

– 1. task announcement:● task generation ● task announcement ● announcing agent

= manager for the

task duration



– 2. bidding process:● agent decides if it is suitable for a task

(eligibility specification; calculating marginal costs)

● suitable: details of

the task are stored;

agent bids for task● manager stores

details of each bid

until deadline



– 3. awarding process:● manager awards task to single bidder● failing agents: delete details of the task● successful bidder must attempt to generate

new subtasks



Working Together: Result Sharing ● Result Sharing:

– agents share information relevant to their subproblems (proactively; reactively)

– improvement:● confidence:

error cross-checking → increasing confidence● completeness:

agents share local views → better overall global view● precision:

result sharing → increasing precision of solution● timeliness:

sharing solutions → result gain more quickly


Working Together ● Coordination:

– management of inter-depencies (agent activities)

→ coordination relationships– negative or positive relationships (benefit from

combining activities) – positive relationships can be requested or non-

requested – assumption:

● coordination at run-time● agents themselves must recognize

relationships; where necessary: management as part of activities


MAKINGDECISIONS

● assumption:

– agents acting/deciding for own good→ each: own preferences/desires about world

– agents = self-interested

→ focus on reaching agreement● Multiagent Interactions:

– each agent: try to increase own utility– actual result depends on particular combination of

actions; each agent: influence the outcome – utility function: preferences depending on how

'good' the possible outcome is for specific agent

Making Decisions: Preferences&Utilities


● idea: two agents simultaneously choose action to perform → creating outcome with selected actions

● assumptions:

– agent has only two choices to make: cooperate or defect (game theory)

– pay-off-matrix:

→ GAME-LIKE ENCOUNTERS

Abb.6: Pay-off-matrix, Wooldrige, 2009


Making Decisions: Preferences&Utilities

● question for each agent: What should I do?● 1. dominance: idea of best response, i.e. strategy with the

highest pay-off no matter what strategy is played (dominant strategy)

● 2. Nash equilibrium (pure strategy):

– stragegies form Nash equilibrium if they are best response to each other

– consider each possible combination of strategies and check if combination forms best response for every agent → pay-off-matrix

– problems: not every scenario a pure strategy Nash equilibrium; sometimes more than one pure strategy Nash equilibrium


Making Decisions: What should I do?

→ SOCIAL CHOICE THEORY (voting theory)

● assumptions:

– agents: own preferences as well as other preferences; then making decisions about how to vote → achieve most preferred outcome

– finite, odd number of voting agents (voters)

→ eliminate possibilities of ties– set of agents tries to rank a finite number of

outcomes by voting


Making Decisions: Group Decisions

● Voting Procedures:

– plurality:

outcome with most votes wins → simple majority voting (two outcomes)

– problem:● outcome wins though other one preferred● voting depend on order of appearance of

outcomes– solution idea:

pair of outcomes: simple majority voting;

winner moves on

→ sequential majority elections



Abb.7: sequentialMajority elections, Wooldrige, 2009



● Auctions:

– efficient agreement on 'allocating scarce ressources' → lacking ressources

– ressources = everything (e.g. processor cycles on pc)

● First-price-sealed-bid auction:

– Single round; agent with highest bid wins– ideal: every agent bids true valuation– Problem:

first place and second hardly any difference

→ solution: bid less than true valuation● Example: online auctions (e.g. Kabash)

Bibliography


● Abb.1: http://www.flickr.com/photos/campuspartymexico/4893260274/

● Abb.2: http://www.flickr.com/photos/learza/25475163/sizes/l/in/photostream/

● Abb.3, Abb.4, Abb.5, Abb.6: Wooldrige, M.(2009). Introduction to Multiagent Systems, Second Edition, Wiley Publishing, p. 224, p. 154, p. 157, p. 228

● Wooldrige, M.(2009). Introduction to Multiagent Systems, Second Edition, Wiley Publishing

multiagent systems – an introduction

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