The similarity assumption in Case-Based Reasoning (similar problems have similar solutions) has been questioned by several researchers. If knowledge about the adaptability of solutions is available, it can be exploited in order to guide retrieval. Several approaches have been proposed in this context, often assuming a similarity or cost measure defined over the solution space. In this paper, we propose a novel approach where the adaptability of the solutions is captured inside a metric Markov Random Field (MRF). Each case is represented as a node in the MRF, and edges connect cases whose solutions are close in the solution space. States of the nodes represent the adaptability effort with respect to the query. Potentals are defined to enforce connected nodes to share the same state; this models the fact that cases having similar solutions should have the same adaptability effort with respect to the query. The main goal is to enlarge the set of potentially adaptable cases that are retrieved (the recall) without significantly sacrificing the precision of retrieval. We will report on some experiments concerning a retrieval architecture where a simple kNN retrieval is followed by a further retrieval step based on MRF inference.

Case-Based Reasoning relies on the underlying hypothesis that similar problems have similar solutions. The extent to which this hypothesis holds good in the case base has been used by CBR designers as a measure of case base complexity, which in turn gives insights on the generalization ability of the reasoner. Several local and global complexity measures have been proposed in the literature. However, the existing measures rely only on the similarity knowledge to compute complexity. We propose a new complexity measure called Reachability-Based Complexity Measure (RBCM) that goes beyond the similarity knowledge to include the effects of all knowledge containers in the reasoner. The proposed measure is evaluated on several real-world datasets and results suggest that RBCM corroborates well with the generalization accuracy of the reasoner.

This paper presents the first results of the research into AI-based support of the room configuration process during the early design phases in architecture. Room configuration (also: room layout or space layout) is an essential stage of the initial design phase: its results are crucial for user-friendliness and success of the planned utilization of the architectural object. Our approach takes into account different possible actions of the configuration process, such as adding, removing, or (re)assigning of the room type. Its mode of operation is based on specific process chain clusters, where each cluster represents a contextual subset of previous configuration steps and provides a recurrent neural network trained on this cluster data only to suggest the next step, and a case base that is used to determine if the current process chain belongs to this cluster. The most similar cluster then tries to suggest the next step of the process. The approach is implemented in a distributed CBR framework for support of early conceptual design in architecture and was evaluated with a high number of process chain queries to prove its general suitability.

In the field of argumentation, the vision of robust argumentation machines is investigated. They explore natural language arguments from available information sources on the web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular query of a user. We aim at combining methods from case-based reasoning (CBR), information retrieval, and computational argumentation to contribute to the foundations of such argumentation machines. In this paper, we focus on the retrieval phase of a CBR approach for an argumentation machine and propose similarity measures for arguments represented as argument graphs. We evaluate the similarity measures on a corpus of annotated micro texts containing different topics and demonstrate the benefit of semantic similarity measures as well as the relevance of structural aspects.

During the early stages of developing Case-Based Reasoning (CBR) systems the definition of similarity measures is challenging since this task requires to transfer implicit knowledge of domain experts into knowledge representations. While an entire CBR system is very explanatory, the similarity measure determines the ranking but do not necessarily show which features contribute to high (or low) rankings. In this paper we will present our work on opening the knowledge engineering process for similarity modelling. We will present how we transfer implicit knowledge from experts as well as a data-driven approach into case and similarity representations for CBR systems. The work present is a result of interdisciplinary research collaborations between AI and medical researchers developing e-Health applications.

Researchers seek to identify biological markers which accurately differentiate cancer subtypes and their severity from normal controls. One such biomarker, DNA methylation, has recently become more prevalent in genetic research studies in oncology. This paper proposes to apply these findings in a study of the diagnostic accuracy of DNA methylation signatures for classifying metastasis samples. Very high classification performance measures were obtained from differentially methylated positions and regions, as well as from selected gene signatures. Perfect accuracy was achieved with the top 5 feature-selected genes using three similar cases and the K-nearest neighbor classfier. This work contributes to the path toward the identification of biological signatures for oncology samples using case-based reasoning.

In this paper, we present RALE-ACL, a communication language for case-based agents in multi-agent systems (MAS) that utilize case-based reasoning (CBR) as the main means of decision making for their agents. RALE-ACL is an accompanying approach of RALE-CBR, a methodology for construction of CBR-based approaches and systems that adds more flexibility to the classic 4R cycle of case-based reasoning. The main goal of RALE-ACL is to establish a much more CBR-compatible alternative to the KQML and FIPA-ACL-based languages, that are currently used in many multi-agent systems, but are too generic and therefore only cumbersomely usable for the specific structure and purposes of case-based agents. This paper is the final part in the trilogy about the RALE methodology.

The main objective of this research is to increase the quality of AI used in commercial RTS games, which has seen little improvement over the past decade. This objective will be addressed by investigating the use of a learning by observation, case-based reasoning agent, which can be applied to new RTS games with minimal development effort. To be successful, this agent must compare favourably with standard commercial RTS AI techniques: it must be easier to apply, have reasonable resource requirements, and produce a better player. Currently, a prototype implementation has been produced for the game StarCraft, and it has demonstrated the need for processing large sets of input data into a more concise form for use at run-time.

We propose a method for obtaining character behavior diversity based on the diversity of plans enacted by characters, and demonstrate this method in a scenario in which characters have multiple choices. Using case-based planning techniques, we reuse plans for varied character behavior, which simulate different personality traits.

Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance was achieved through the use of case-based reasoning. In this framework, the agent optimally solves a set of problems and stores their solutions in a case base. Then, given any new problem, it seeks a similar case in the case base and uses its solution as an aid to solve the problem at hand. A number of ad hoc approaches to the case base formation problem have been proposed and empirically shown to perform well. In this paper, we investigate a theoretically driven approach to solving the problem. We mathematically relate properties of a case base to the suboptimality of the solutions it produces and subsequently develop an algorithm that addresses these properties directly. An empirical evaluation shows our new algorithm outperforms the existing state of the art on contemporary video-game pathfinding benchmarks.