Transcript COSC 4117 Artificial Intelligence

Distributed AI
an overview
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
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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
degree of
commonality
or conflict of
goals
amount of
interaction
between
agents
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 environment
agents 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
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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
pure conflict
between
goals
no
interaction
between
agents
ongoing operation
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Example environments
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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
degree of
commonality
or conflict of
goals
TOD
SOD/WOD
amount of
interaction
between
agents
single or ongoing operation
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Negotiation mechanisms
 the negotiation system provided by the
environment
 desirable properties of negotiation
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‘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)
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