1. Adaptive Choice of Information Sources
Adaptive Choice of Information Sources
권권택

2. 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

3. 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

4. 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.

5. Multiple adaptive agents
Information Resources
r-window
Agents

6. 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

7. 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

8. 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

10. 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

11. 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

12. Probabilistic Analysis

13. Probabilistic Analysis

14. 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%

15. 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

16. A Model-based Approach
G-agents : greedy agents
P-agents : probability function agents
M-agents : modeling agents

20. 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

21. 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

23. Results
The MEU strategy outperforms the most-often-liked heuristic when the probability distribution for the search engines are skewed

24. 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