Learning by observation allows a software agent to learn by watching an expert perform a task. This transfers the burden of training from the expert, who would traditionally need to program the agent, to the agent itself. Most existing approaches to learning by observation perform their observation in a purely passive manner. We propose a case-based reasoning agent that is able to observe passively but can also use mixed-initiative control to request assistance from the expert for difficult input problems. Our agent uses mixed-initiative case acquisition in the game of Tetris. We show that the agent is able to obtain cases it would not have been able to with passive observation alone, is able to improve its performance and places less burden on the expert.

The combination of learning by demonstration and planning has proved an effective solution for real-time strategy games. Nevertheless, learning hierarchical plans from expert traces also has its limitations regarding the number of training traces required, and the absence of mechanisms for rapidly reacting to high priority goals. We propose to bring the game designer back into the loop, by allowing him to explicitly inject decision making knowledge, in the form of behavior trees, to complement the knowledge obtained from the traces. By providing a natural mechanism for designers to inject knowledge into the plan library, we intend to integrate the best of both worlds: learning from traces and hard-coded rules.

We present an efficient random decision tree algorithm for case-based reasoning systems. We combine this algorithm with a simple similarity measure based on domain knowledge to create a stronger hybrid algorithm. This combination is based on our general approach for combining lazy and eager learning methods. We evaluate the resulting algorithms on a case base of patient records in a palliative care domain. Our hybrid algorithm consistently produces a lower average error than the base algorithms.

Case-based reasoning (CBR) is an artificial intelligence problem solving and analysis methodology that retrieves and adapts previous experiences to fit new contexts. In CBR, a system's expertise is embodied in a library of past cases, rather than being encoded in classical rules. A new problem is solved by finding a similar past case, and reusing it in the new problem situation. Therefore, the knowledge and reasoning process used by an expert to solve the problem is not recorded, but is implicit in its solution. The CBR field has grown rapidly over the last few years, as seen by its increased share of papers at major conferences, available commercial tools, and successful applications in daily use.

Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and/or learning on target problems. In transfer learning, the differences between the source and target problems characterize the transfer distance. CBR can support transfer learning methods in multiple ways. We illustrate how CBR and transfer learning interact and characterize three approaches for using CBR in transfer learning: (1) as a transfer learning method, (2) for problem learning, and (3) to transfer knowledge between sets of problems.

Case-based reasoning (CBR) is a problem-solving process in which a new problem is solved by retrieving a similar situation and reusing its solution. Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and/or learning on target problems. In transfer learning, the differences between the source and target problems characterize the transfer distance. CBR can support transfer learning methods in multiple ways. We illustrate how CBR and transfer learning interact and characterize three approaches for using CBR in transfer learning: (1) as a transfer learning method, (2) for problem learning, and (3) to transfer knowledge between sets of problems. We describe examples of these approaches from our own and related work and discuss applicable transfer distances for each. We close with conclusions and directions for future research applying CBR to transfer learning.

This issue summarizes the state of the art in structured knowledge transfer, which is an emerging approach to the general problem of knowledge acquisition and reuse. Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain.

One challenge of building and maintaining large-scale data management systems is managing data fusion from multiple data sources. Often times, different data sources may represent the same data element in a slightly different way. These differences may represent an error in the data or a disagreement between sources on the correct value that best represents the data point. When the quantity of data managed and fused becomes sufficiently large, manual review becomes impossible, and automated systems must be built to manage data fusion. Some of the traditional solutions use simple voting theory, Dempster-Shafer theory, fuzzy matching and incremental learning. This paper presents a novel approach to data fusion in the domain of business listings. The task at hand, business listing categorization, suffers from conflicting and incomplete data from disparate data sources. Given the need for a high degree of accuracy in this task, we use a combination of case-based reasoning, joint probability, and domain-specific rules to improve data accuracy above other methods.

The paper describes the first phase of development of a Case-Base Reasoning (CBR) system to support early conceptual design of buildings. As specific context of application, the research focuses on energy performance of commercial buildings, and the early identification of energy-related features that contribute to its outcomes. The hypothesis is that bringing knowledge from relevant precedents may facilitate this identification process, thus offering a significant contribution for early analysis and decision-making. The paper introduces a proof-of-concept for such a system, proposing a novel integration of Case-Based Reasoning, Parametric Modeling (Building Information Modeling), and Ontology Classification. Potential advantages and limitations of this three-level integration approach are discussed along with recommendations for future development.

We describe Cobot, a mixed initiative socio-semantic conversational search and recommendation system for finding health information. With Cobot, users can start a real time conversation about their health concerns. Cobot then connects relevant users together in the conversation also providing contextual recommendations relevant to the conversation. Conventional search engines and content portals provide a solitary search experience inundating the health information seeker with a hoard of information often confusing and frustrating them. Cobot brings relevant healthcare information directly or through other users without any search through natural language conversation.