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Unstructured Agent Matchmaking Experiments in Timing and Fuzzy Matching Elth Ogston and Stamatis Vassiliadis Computer Engineering Laboratory TU Delft.

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Presentation on theme: "Unstructured Agent Matchmaking Experiments in Timing and Fuzzy Matching Elth Ogston and Stamatis Vassiliadis Computer Engineering Laboratory TU Delft."— Presentation transcript:

1 Unstructured Agent Matchmaking Experiments in Timing and Fuzzy Matching Elth Ogston and Stamatis Vassiliadis Computer Engineering Laboratory TU Delft

2 Are there elements of coordination within large multi-agent systems that can be obtained “for free”? i.e. Without complicated agent algorithms (planning, scheduling, intelligence) Without external structure (facilitators, directories, blackboards, know topology)

3 Matchmaking: how do agents that require an outside service find other agents who are willing to provide that service? Assume redundancy of providers and consumers (MAS are open, flexible and component based) Simple agents Coordination without outside help How studied? - Simulation of an abstract model. Results? - We find that there are conditions under which simple unaided agents do find matches This paper – checking two further conditions, timing and how matches are determined

4 General philosophy Overview of our model and previous results Some new results on timing and forms of matching Summary of further work Talk Organization

5 Philosophy - scalability Multi-agent systems can in theory be world/internet size. Humans tend to believe in (central) control (God, aliens, The FBI, Mom) However they often make use of systems components, like directories, that don’t scale well… why? Scientists and engineers who design computers are trained to see order in the world.

6 Philosophy – sloppy systems Natural systems tend to be redundant and full of failures. Lets try looking at coordination not as beautifully interlocking clockwork but as an cloud that just happens to look like an elephant when you squint a bit, turn it upside down, and ignore that part over there….

7 Philosophy – matchmaking thought experiment How would you find someone else in the room with the same number? Broadcast Now scale up, find someone in Madrid with a number between 1 and 100,000 Broker Ask your neighbors Imagine a number between 1 and 10.

8 Agents Model - Components Connections Tasks Categories

9 Model -Movement “Cluster” “Shuffle” A B C A B C

10 Model - Characteristics There are several “good” matches available We aren’t looking for the global best match Not all agents need to be successful No centralized directory No predefined structure Agents are simple Agents only know about their immediate surroundings

11 Previous Results Matches are found –Limited by the number of task categories and the number of neighbors to each agent Limiting cluster size creates a distributed system Replacing tasks creates a dynamic system

12 New Results System timing doesn’t play a role in coordination Fuzzy probabilistic category matches produce the same behavior as discreet deterministic matches

13 Agents moving in sync vs. agents moving in a random order

14 Deterministic matches vs. probabilistic matches

15 Further Work AAMAS 2002 – comparison of a peer-to- peer auction with a centralized auction –P2P shows same auction behavior –As we add more agents P2P has constant message costs vs. linear for a central auctioneer

16 More Info….


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