Adaptive Choice of Information Sources 2019-04-25 Adaptive Choice of Information Sources 96419-006 권권택

Introduction Adaptive Information Agent 정보를 수집, 가공해서 제공 사용자 취향을 학습, 저장, 가공 변화하는 환경에 유연하게 대응 We assume that agents don’t have significant control on the composition of other agents the loads on information sources they use 2019-04-25

The Goal of Adaptive Agents Decrease response time Avoid congestion in information sources Improve stability Converge to balanced, stable configuration Improve information quality Balance exploration and exploitation 2019-04-25

A Categorization of Approaches Adaptive schemes to be used by multiple information agents ; Identify lightly loaded resources. State-based solution base decision on the observed load distribution. Model-based solution consider not only the state, but also the expected behavior of other agents. 2019-04-25

Multiple adaptive agents Information Resources r-window Agents 2019-04-25

A Categorization of Approaches Adaptive schemes to be used by a stand-alone information agents Optimize the quality of information Learn about the different expertise levels or specializations of the information sources 2019-04-25

A State-based Approach The basic assumptions all loads can provide the same information response time of a source increases with its workload no explicit communication between agents 2019-04-25

A State-based Approach r-window a window through which an agent can observe the load on some resources At each time step, each agents have to decide whether continue to use the present resource or move to another resource in its r-window Use a probabilistic decision procedure 2019-04-25

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Results If agents are allowed access to the status of smaller number of resources, the loads on different resources are balanced in less time Convergence rate to stable configurations can be significantly enhanced if local group make their decisions sequentially 2019-04-25

Probabilistic Analysis X : number of agents who will not leave the resource in the next time step Y : number of agents who will move into the resource i in the next time step 2019-04-25

Probabilistic Analysis 2019-04-25

Probabilistic Analysis 2019-04-25

Adaptive Agents Initially use a large r-window size, but quickly reduce this size after some initial movements Improvements of adaptive scheme Skewed initial distribution : 21% Uniform initial distribution : 3% 2019-04-25

A Model-based Approach Modeling agent decision functions using Chebychev polynomials Each agent observes the load on the resource in which another agent places a job, the number of previous visits in which it did not place the job Modeling agents will use the information and find resources less likely to be selected 2019-04-25

A Model-based Approach G-agents : greedy agents P-agents : probability function agents M-agents : modeling agents 2019-04-25

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Results Addtion of M-agents reduces the standard deviation of loads across the resources Homogeneous group of M-agents do not produce effective performance Modeling scheme is able to track changes in agent behaviors 2019-04-25

Learning to Select Information Sources Different search engines are good for different kind of queries The performance of the search engine are modeled probabilistically from experience the principle of Maximum Expected Utility an agent chooses an action that yields the highest expected utility, averaged over all the possible outcomes 2019-04-25

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Results The MEU strategy outperforms the most-often-liked heuristic when the probability distribution for the search engines are skewed 2019-04-25

Conclusions Agents that don’t model other agents can be made to converge faster to stable distributions by introducing asynchrony. When we evaluate a mix of adaptive and static-strategy agents, everyone benefits Agents using expected utility maximizing paradigm can be used when quality information has to be returned in real-time 2019-04-25