Distributed AI

1. Distributed AI an overview

2. 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 D Goforth - COSC 4117, fall 2003

3. 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 D Goforth - COSC 4117, fall 2003

4. 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 D Goforth - COSC 4117, fall 2003

5. 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’ D Goforth - COSC 4117, fall 2003

6. Distributed systems – problem space degree of commonality or conflict of goals amount of interaction between agents single or ongoing operation D Goforth - COSC 4117, fall 2003

7. 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 D Goforth - COSC 4117, fall 2003

8. Internet artificial environments • distributed solutions – web crawlers • artificial environments to enable distributed solutions – auction and bid software D Goforth - COSC 4117, fall 2003

9. Internet artificial environments policy and common goals  ‘rules’ of environment agents act to achieve individual goals within rules achieve common policy goals also D Goforth - COSC 4117, fall 2003

10. eBay • environment • parallel auctions – auction search engine • extended but fixed bidding interval • large potential bidding audience • agents • bidding agent D Goforth - COSC 4117, fall 2003

11. 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 D Goforth - COSC 4117, fall 2003

12. 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 D Goforth - COSC 4117, fall 2003

13. 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) D Goforth - COSC 4117, fall 2003

14. 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) D Goforth - COSC 4117, fall 2003

15. 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 D Goforth - COSC 4117, fall 2003

16. 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) D Goforth - COSC 4117, fall 2003

17. Low cost telephone service in problem space pure conflict between goals no interaction between agents ongoing operation D Goforth - COSC 4117, fall 2003

18. Example environments • Electric power grids • Robots on assembly line • Bank transactions • Traffic flow • Distributed computing positions in problem space? D Goforth - COSC 4117, fall 2003

19. What is DAI? • AI (intelligent agent) • game theory (interaction of agents) • distributed computing D Goforth - COSC 4117, fall 2003

20. 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 D Goforth - COSC 4117, fall 2003

21. Negotiation problem example domains • Task-oriented domains • State-oriented domains • Worth-oriented domains D Goforth - COSC 4117, fall 2003

22. 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) D Goforth - COSC 4117, fall 2003

23. 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 D Goforth - COSC 4117, fall 2003

24. Worth-oriented domains • generalized S-ODs – value function defines value of every state for agent • possibility of efficient solutions with compromise – search model D Goforth - COSC 4117, fall 2003

25. Negotiation problem example domains degree of commonality or conflict of goals TOD amount of interaction between agents SOD/WOD single or ongoing operation D Goforth - COSC 4117, fall 2003

26. 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) D Goforth - COSC 4117, fall 2003