Collaborative Case Retention Strategies for CBR Agents
نویسندگان
چکیده
Empirical experiments have shown that storing every case does not automatically improve the accuracy of a CBR system. Therefore, several retain policies have been proposed in order to select which cases to retain. However, all the research done in case retention strategies is done in centralized CBR systems. We focus on multiagent CBR systems, where each agent has a local case base, and where each agent can interact with other agents in the system to solve problems in a collaborative way. We propose several case retention strategies that directly deal with the issue of being in a multiagent CBR system. Those case retention strategies combine ideas from the CBR case retain strategies and from the active learning techniques. Empirical results show that strategies that use collaboration with other agents outperform those strategies where the agents work in isolation. We present experiments in two different scenarios, the first one allowing multiple copies of one case and the second one only allowing one copy of each case. Although it may seem counterintuitive, we show and explain why not allowing multiple copies of each case achieves better results.
منابع مشابه
Justification-Based Case Retention
A CBR system needs a good case retention strategy to decide which cases to incorporate into the case base in order to maximize the performance of the system. In this work we present a collaborative case retention strategy, designed for multiagent CBR systems, called the Collaborative Case Bargaining strategy. The CCB strategy is a bargaining mechanism in which each CBR agent tries to maximize t...
متن کاملEnsemble case based learning for multi-agent systems
This monograph presents a framework for learning in a distributed data scenario with decentralized decision making. We have based our framework in MultiAgent Systems (MAS) in order to have decentralized decision making, and in Case-Based Reasoning (CBR), since the lazy learning nature of CBR is suitable for dynamic multi-agent systems. Moreover, we are interested in autonomous agents that colla...
متن کاملCase-based Learning from Proactive Communication
We present a proactive communication approach that allows CBR agents to gauge the strengths and weaknesses of other CBR agents. The communication protocol allows CBR agents to learn from communicating with other CBR agents in such a way that each agent is able to retain certain cases provided by other agents that are able to improve their individual performance (without need to disclose all the...
متن کاملAn Architecture for Multiple Heterogeneous Case-Based Reasoning Employing Agent Technologies
This paper presents an investigation into applying Case-Based Reasoning to Multiple Heterogeneous Case Bases using agents. The adaptive CBR process and the architecture of the system are presented. A case study is presented to illustrate and evaluate the approach. The process of creating and maintaining the dynamic data structures is discussed. The similarity metrics employed by the system are ...
متن کاملCBR and Argument Schemes for Collaborative Decision Making
In this paper we present a novel approach for combining Case-Based Reasoning (CBR) and Argumentation. This approach involves 1) the use of CBR for evaluating the arguments submitted by agents in collaborative decision making dialogs, and 2) the use of Argument Schemes and Critical Questions to organize the CBR memory space. The former involves use of past cases to resolve conflicts among newly ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003