Satellite imagery is now widely used in the defense sector for monitoring locations of interest. Although the increasing amount of data enables pattern identification and therefore prediction, carrying this task manually is hardly feasible. We hereby propose a cased-based reasoning approach for automatic prediction of rare events on strategic sites. This method allows direct incorporation of expert knowledge, and is adapted to irregular time series and small-size datasets. Experiments are carried out on two use-cases using real satellite images: the prediction of submarines arrivals and departures from a naval base, and the forecasting of imminent rocket launches on two space bases. The proposed method significantly outperforms a random selection of reference cases on these challenging applications, showing its strong potential. Keywords: Predictive analysis · Case-based reasoning · Earth observation · Submarine activity · Space launch.

We investigate case-based reasoning (CBR) problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. To this end, we employ abstract argumentation (AA) and propose a novel methodology for CBR, called AA-CBR. The argumentative formulation naturally allows to characterise the computation of an outcome as a dialogical process between a proponent and an opponent, and can also be used to extract explanations for why an outcome for a new case is (not) computed.

The addition of a robot to a team can be difficult if the human teammates do not trust the robot. This can result in underutilization or disuse of the robot, even if the robot has skills or abilities that are necessary to achieve team goals or reduce risk. To help a robot integrate itself with a human team, we present an agent algorithm that allows a robot to estimate its trustworthiness and adapt its behavior accordingly. As behavior adaptation is performed, using case-based reasoning (CBR), information about the adaptation process is stored and used to improve the efficiency of future adaptations.

This paper presents a case-based reasoning (CBR) application for discovering similar patients with non-specific musculoskeletal disorders (MSDs) and recommending treatment plans using previous experiences. From a medical perspective, MSD is a complex disorder as its cause is often bounded to a combination of physiological and psychological factors. Likewise, the features describing the condition and outcome measures vary throughout studies. However, healthcare professionals in the field work in an experience-based way, therefore we chose CBR as the core methodology for developing a decision support system for physiotherapists which would assist them in the process of their co-decision making and treatment planning. In this paper, we focus on case representation and similarity modeling for the non-specific MSD patient data as well as we conducted initial experiments on comparing patient profiles.

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.

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.