Before laboratory testing, bioinformatics problems often require a machine-learned predictor to identify the most likely choices among a wealth of possibilities. Researchers may advocate different predictors for the same problem, none of which is best in all situations. This paper introduces a case-based meta-predictor that combines a set of elaborate, pre-existing predictors to improve their accuracy on a difficult and important problem: protein-ligand docking. The method focuses on the reliability of its component predictors, and has broad potential applications in biology and chemistry. Despite noisy and biased input, the method outperforms its individual components on benchmark data. It provides a promising solution for the performance improvement of compound virtual screening, which would thereby reduce the time and cost of drug discovery.

Recent years have witnessed the success of hashingtechniques in approximate nearest neighbor search. Inpractice, multiple hash tables are usually employed toretrieve more desired results from all hit buckets ofeach table. However, there are rare works studying theunified approach to constructing multiple informativehash tables except the widely used random way. In thispaper, we regard the table construction as a selectionproblem over a set of candidate hash functions. Withthe graph representation of the function set, we proposean efficient solution that sequentially applies normal-ized dominant set to finding the most informative andindependent hash functions for each table. To furtherreduce the redundancy between tables, we explore thereciprocal hash tables in a boosting manner, where thehash function graph is updated with high weights em-phasized on the misclassified neighbor pairs of previoushash tables. The construction method is general andcompatible with different types of hashing algorithmsusing different feature spaces and/or parameter settings.Extensive experiments on two large-scale benchmarksdemonstrate that the proposed method outperforms bothnaive construction method and state-of-the-art hashingalgorithms, with up to 65.93% accuracy gains.

Planning is a notoriously difficult computational problem of high worst-case complexity. Researchers have been investing significant efforts to develop heuristics or restrictions to make planning practically feasible. Case-based planning is a heuristic approach where one tries to reuse previous experience when solving similar problems in order to avoid some of the planning effort. Plan reuse may offer an interesting alternative to plan generation in some settings. We provide theoretical results that identify situations in which plan reuse is provably tractable. We perform our analysis in the framework of parameterized complexity, which supports a rigorous worst-case complexity analysis that takes structural properties of the input into account in terms of parameters. A central notion of parameterized complexity is fixed-parameter tractability which extends the classical notion of polynomial-time tractability by utilizing the effect of parameters. We draw a detailed map of the parameterized complexity landscape of several variants of problems that arise in the context of case-based planning. In particular, we consider the problem of reusing an existing plan, imposing various restrictions in terms of parameters, such as the number of steps that can be added to the existing plan to turn it into a solution of the planning instance at hand.

There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain models. In contrast, our approach views the plan generated with respect to the incomplete model as a ``skeletal plan'' and augments it with directed mining of plan fragments from library cases. We will present the details of our approach and present an empirical evaluation of our method in comparison to a state-of-the-art case-based planner that depends on complete domain models.

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.

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.

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

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.