Download - 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)
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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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 8
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 10
Working Together: CDPS
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 11
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 12
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)
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 13
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 14
Working Together: Task Sharing
– 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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 15
Working Together: Task Sharing
– 3. awarding process:● manager awards task to single bidder● failing agents: delete details of the task● successful bidder must attempt to generate
new subtasks
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 16
Working Together: Task Sharing
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 17
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 18
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 20
● 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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 21
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 22
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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 23
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
Making Decisions: Group Decisions
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 24
Abb.7: sequentialMajority elections, Wooldrige, 2009
Making Decisions: Group Decisions
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 25
● 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
10.Mai 2012 LMU, PST | Prof. Dr. Wirsing, PS Adaptive Agenten, SoSe 2012, C. Kroiß, H.L.Nguyen Vo 26
● 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