Distributed AI

an overview

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Why distributed AI? ‘situated expert’ – the importance of general

knowledge and incorporation of distinct points of view – CYC human problem-solving teams with different

expertise (and representations!) complexity of problems requires

decomposition – OOP distributed problems – decentralized problem-

solving – internet, air-traffic control

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Multi-agent systems

parallel action at some level emergent structure

chemical – pressure and temperature biological – bee hives mathematical – fractals

artificial organization decentralized multi-agent systems emergent solution to problems

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Multi-agent systems

agents in environment agents each interact with

environment (perception, action) agents interact with each other

levels of interaction vary independent influence through environment direct communication

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Multi-agent system problems

agents have distinct / common goals independent competitive (can interfere with each other) cooperative (can help each other) collaborative agents have common goal

‘one shot’ problems or ongoing ‘survival’

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Distributed systems – problem space

amount of interaction between agents

degree of commonality or conflict of

goals

single or ongoing operation

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Emergent solutions - examples

efficient traffic flow based on actions of individual agents

powerful search engine based on web-crawling agents

just-in-time delivery and minimal inventory

eBay

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Internet artificial environments

distributed solutions – web crawlers artificial environments to enable

distributed solutions – auction and bid software

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Internet artificial environments

policy and common goals ‘rules’ of environmentagents act to achieve individual goals within rules

achieve common policy goals also

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eBay

environment parallel auctions – auction search engine extended but fixed bidding interval large potential bidding audience

agents bidding agent

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Example – low cost telephone service in artificial market place

current problem competition based on service plans

hard to understand and compare constrains complexity of cost/service

structure waste of resources on advertising

(instead of cost reduction or service improvement)

difficult for new service providers to enter market

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Low cost telephone service in automated negotiating environment

two classes of agent: service providers customers’ telephones

environment - phonecall marketplace intelligent telephone requests service service providers submit offers telephone selects one offer and connects

to service provider market handles accounting and billing

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Low cost telephone service in automated negotiating environment

advantages competitive on service and rate no ‘service plans’ to understand since no

long term commitment easy for service providers to change pricing easy for service providers to enter market intelligent telephone agent maximizes self

interest (min cost for req’d service) service providers maximize self interest

(maximize profit)

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Low cost telephone service in automated negotiating environment

designing the environment how is bidding managed?

goal get companies to bid the lowest price

they can offer get companies NOT to bid strategically

(bid maximum they think will win)

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Low cost telephone service in automated negotiating environment

strategic bidding consider what others will bid

operate ‘customer agents’ to elicit offers from other service providers

bid just less than competition

how to suppress strategic bidding Vickrey’s mechanism

lowest bid wins lowest bidder is paid at second lowest rate

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Vickrey’s mechanism example

A bids to provide service at 10¢ / min B bids to provide service at 12¢ / min all other bids higher A wins contract, paid 12¢ / min

rationale – incentive to relate bid to true cost no incentive to underbid (might win and have to

provide service at a loss) no incentive to overbid (might lose unnecessarily

and no gain in profit otherwise)

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Low cost telephone service in problem space

no interaction between agents

pure conflict between

goals

ongoing operation

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Example environments

Electric power grids Robots on assembly line Bank transactions Traffic flow Distributed computing

positions in problem space?

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What is DAI?

AI (intelligent agent) game theory (interaction of agents) distributed computing

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Negotiation problem

environment: communication between agents language of communication – protocols

agents: goals tactics – using protocols to achieve goals

how to achieve the best deal concessions, lies, threats

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Negotiation problem example domains

Task-oriented domains State-oriented domains Worth-oriented domains

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Task-oriented domains

Agents can act independently Agents can’t interfere with each other Only incentive is possible cost reduction

by cooperation (e.g., school boards sharing school bus routes)

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State-oriented domains

Each agent has goal of environment in certain state

Agents can interfere with each other – goal states in conflict or with mutual goal at high cost (limited resources)

Incentive to negotiate – concede some goals; pay extra cost

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Worth-oriented domains

generalized S-ODs – value function defines value of every state for agent

possibility of efficient solutions with compromise – search model

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Negotiation problem example domains

amount of interaction between agents

degree of commonality or conflict of

goals

single or ongoing operation

TODSOD/WOD

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Negotiation mechanisms the negotiation system provided by the

environment desirable properties of negotiation

‘global optimality’ – policy goal efficiency – don’t waste agent resources stability – no incentive to leave a deal distributed – no central ‘authority’ required fairness – no preference based on external

properties (not symmetry)