This paper reports preliminary work on developing methods automatically to index cases described in text so that a case-based reasoning system can reason with them. We are employing machine learning algorithms to classify full-text legal opinions in terms of a set of predefined concepts. These factors, representing factual strengths and weaknesses in the case, are used in the casebased argument...

In this paper we present an efficient hybrid classification algorithm based on combining case-based reasoning and random decision trees, which is based on a general approach for combining lazy and eager learning methods. We use this hybrid classification algorithm to predict the pain classification for palliative care patients, and compare the resulting classification accuracy to other similar ...

Since RoboCup 2004 we participate in both, the “classical” 2D-Simulation League as well as in the new 3D-competition. Last year, our team “AT Humboldt”, placed second at German Open while “AT Humboldt 3D” placed 8th at RoboCup World Championship in Osaka. For ten years we use our soccer agents as a research testbed for longterm deliberation and realtime reasoning, cooperation and coordination i...

This paper presents a navigation component based on a hybrid case-based reasoning (CBR) and reinforcement learning (RL) approach for an AI agent in a real-time strategy (RTS) game. Spatial environment information is abstracted into a number of influence maps. These influence maps are then combined into cases that are managed by the CBR component. RL is used to update the case solutions which ar...

This paper focuses on integrating inductive inference and case-based reasoning. We study integration along two dimensions: Integration of case-based methods with methods based on general domain knowledge, and integration of problem solving and incremental learning from experience. In the INRECA system, we perform case-based reasoning as well as TDIDT (TopDown Induction of Decision Trees) classi...

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...

CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates ‘‘learning’’ from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification....

Biofeedback is a method gaining increased interest and showing good results for a number of physical and psychological problems. Biofeedback training is mostly guided by an experienced clinician and the results largely rely on the clinician’s competence. In this paper we propose a three phase computer assisted sensor-based biofeedback decision support system assisting less experienced clinician...

Humans usually use information about previous experiences to solve new problems. Following this principle, we propose an approach to enhance a multi-agent system by including an authority that generates new regulations whenever new conflicts arise. The authority uses a unsupervised version of classical Case-Based Reasoning to learn from previous similar situations and generate regulations that ...

Locomotives are complex electromechanical systems. Continuously monitoring the health state of locomotives is critical in modern cost-effective maintenance strategy. A typical locomotive is equipped with the capability to monitor their state and generate fault messages and a snapshot of sensed parametric readings in response to anomalous conditions. In our previous studies, we have developed an...