The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multilayered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the G...

be a problem when applying CBR to weak-theoretic domains.1 The knowledge elicitation bottleneck—the inability to precisely encode the knowledge used by human experts—is a concern in many knowledge-based applications. Although researchers cite this bottleneck as a justification for CBR techniques,2 use of domain knowledge in indexing means that CBR techniques are not immune to it. We’ve develope...

The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The interpretive process is considered as an intrinsic part in OSHAM and is carried...

Although similarity measures play a crucial role in CBR applications, clear methodologies for defining them have not been developed yet. One approach to simplify the definition of similarity measures involves the use of machine learning techniques. In this paper we investigate important aspects of these approaches in order to support a more goal-directed choice and application of existing appro...

A common cause of retrieval failure in case-based reasoning (CBR) approaches to product recommendation is that the retrieved cases, usually those that are most similar to the target query, are not sufficiently representative of compromises that the user may be prepared to make. We present a new approach to retrieval in which similarity and compromise play complementary roles, thereby increasing...

This paper presents a hierarchical approach to the problems inherent in parts of real-time strategy games. The overall game is decomposed into a hierarchy of sub-problems and an architecture is created that addresses a significant number of these through interconnected machinelearning (ML) techniques. Specifically, individual modules that use a combination of case-based reasoning (CBR) and rein...

Defining similarity measures is a crucial task when developing CBR applications. Particularly, when employing utility-based similarity measures rather than pure distance-based measures one is confronted with a difficult knowledge engineering task. In this paper we point out some problems of the state-of-the-art procedure to defining similarity measures. To overcome these problems we propose an ...

We present an approach that uses learning from demonstration in a computer role playing game to create a controller for a companion team member. We describe a behavior engine that uses case-based reasoning. The behavior engine accepts observation traces of human playing decisions and produces a sequence of actions which can then be carried out by an artificial agent within the gaming environmen...