The paper deals with the application of the Case-Based Reasoning Paradigm (CBR) in Design Support Systems in Architecture. Based on the finding that promising concepts and systems do exist in architecture the question as to why they do not gain the anticipated success is explored. In search for reasons a comprehensive comparison between the cognitive model and the derived conceptual method, theoretical contemplations of architectural design as well as the actual application of the method in CBR systems in Architecture, manifests the core of the work presented.

The success of the Case Based Reasoning (CBR) system depends on the quality of the case data. This quality is dedicated to the study of the case base competence which is measured by the range of problems that can be satisfactorily solved. In fact, modeling case-base competence is a clamorous issue in the discipline of CBR. However, the existence of erroneous cases as noises and the non uniform problem distributions has not been considered in the proposed computing competence. In this paper, we proposea novel case base competence model based on Mahalanobis distance and a clustering technique named DBSCAN-GM. The advantage of this newly proposed model is its high accuracy for predictingcompetence. In addition, it is not sensitive to noisy cases and it takes account the situation of the distributed case-base.Withal, we contest that this model has aconspicuous role to play in future CBR research infields such as the development of new policies for maintainingthe case base.

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 environment. Our work focuses on team-based role playing games, where the agents produced by the behavior engine act as team members within a mixed human-agent team. We present the results of a study we conducted, where we assess both the quantitative and qualitative performance difference between human-only teams compared with hybrid human-agent teams. The results of our study show that human-agent teams were more successful at task completion and, for some qualitative dimensions, hybrid teams were perceived more favorably than human-only teams.

Traces of user behaviour can be a valuable source of knowledge that can be used during case-based reasoning. This paper presents an approach for analyzing and cleaning user traces. The analysis looks to identify three properties in traces: reasoning with an internal state, non-deterministic behaviour and error. The existence of any of these properties may influence how a system should reason or store knowledge in cases. Initially, each trace is examined to see areas that might contain one of the three properties. Multiple versions of the trace are then generated in order to determine which specific property is present. The analysis is applied to traces generated by observing both a computer and human controller for an obstacle avoidance robot. The results demonstrate that the analysis is able to successfully identify which properties are present and clean many of the errors that exist in the traces.

This paper addresses Trace-Based Reasoning (TBR) by using Case-Based Reasoning (CBR) as a descriptive framework. TBR is a reasoning paradigm in which inferences are made on specific objects called traces. Traces are sequential records of events observed and stored during an interactive process. We report two contributions. First, we propose a review of the current researches related to TBR. Then, we compare CBR and TBR. From this comparison, we show that the exploitation of traces instead of cases as knowledge sources raises very specific challenges. More precisely, new methods for defining similarity measures and for performing adaptation of traces are required. These new methods have to take into account the sequential properties of traces. We emphasis the benefits of using traces as a knowledge container in a reasoning process and we pinpoint promising applications of TBR.

Introduction to the papers presented at the 2013 FLAIRS Conference Special Track on Case-Based Reasoning

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.

In this article we present DrillEdge -- a commercial and award winning software system that monitors oil-well drilling operations in order to reduce non-productive time (NPT). DrillEdge utilizes case-based reasoning with temporal representations on streaming real-time data, pattern matching and agent systems to predict problems and give advice on how to mitigate the problems. The methods utilized, the architecture, the GUI and development cost in addition to two case studies are documented.

In this article we present DrillEdge — a commercial and award winning software system that monitors oil-well drilling operations in order to reduce non-productive time (NPT). DrillEdge utilizes case-based reasoning with temporal representations on streaming real-time data, pattern matching and agent systems to predict problems and give advice on how to mitigate the problems. The methods utilized, the architecture, the GUI and development cost in addition to two case studies are documented.

IAAI-14 will be held July Sixth Annual Symposium on Combinatorial 27-31, 2014, in Quebec City, Quebec, Search. SoCS 2013 will be AAAI Spring Symposium Series. ICINCO 2013 will be Seventh International AAAI Conference on Weblogs and Social Media. Twenty-Sixth International FLAIRS held July 28-31, 2013 in Reykjavík, ICWSM-13 will be held July 8-11, 2013 Conference. Twenty-Seventh AAAI Conference on Twenty-Third International Conference COGSCI 2013 will be held July 31 - Artificial Intelligence and Twenty-on Automated Planning and August 3, 2013 in Berlin, Germany Fifth Innovative Applications of Artificial Scheduling.