A hierarchical classifier (cascade) is proposed for target detection. In building an optimal cascade we considered three heuristics: (1) use of a frontier-following approximation, (2) controlling error rates, and (3) weighting. Simulations of synthetic data with various underlying distributions were carried out. We found that a weighting heuristic is optimal in terms of both computational compl...

In this paper we concentrate on practical aspects of qualitative modeling and reasoning, reporting our experience within the VMBD project 1 in applying Constraint Programming techniques to the task of diagnosing a real-life automotive subsystem. We propose a layered modeling approach: qualitative deviations equations as high level model and Constraint Satisfaction Problems (CSPs) with non binar...

This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyperheuristics has recently been employed to refer to “heuristics that choose heuristics” rather than heuristics that operate directly on given problems. One of the overriding motivations of hyper-heuristic methods is the attempt to develop techniques that can ope...

Clustering techniques have been used by many intelligent software agents in order to retrieve lter and categorize documents available on the World Wide Web Clustering is also useful in extracting salient features of related web documents to automatically formulate queries and search for other similar documents on the Web Traditional clustering algorithms either use a priori knowledge of documen...

With a growing amount of electronic documents available, there is a need to classify documents automatically. In growing text classification applications, important-term selection is a critical task for the classifier performance. Although many different techniques and heuristics have been developed, this paper shows that many of them are just a sub-set of more advanced methods originating in t...

In this paper we focus on efficient methods for pruning the state space in costoptimal planning. The use of heuristics to guide search and prune irrelevant branches has been widely and successfully explored. However, heuristic computation at every node in the search space is expensive, so reducing the number of nodes to be considered by up-front analysis has great potential. Our contributions a...

Symbolic PDBs and Merge-and-Shrink (M&S) are two approaches to derive admissible heuristics for optimal planning. We present a combination of these techniques, Symbolic Merge-and-Shrink (SM&S), which uses M&S abstractions as a relaxation criterion for a symbolic backward search. Empirical evaluation shows that SM&S has the strengths of both techniques deriving heuristics at least as good as the...

Abstract Quantum mechanics is well known to accelerate statistical sampling processes over classical techniques. In quantitative finance, samplings arise broadly in many use cases. Here we focus on a particular one of such cases, credit valuation adjustment (CVA), and identify opportunities challenges towards quantum advantage for practical instances. To build NISQ-friendly circuit able solve p...

The conflict resolution method, introduced by the authors in [1] is a new method for solving systems of linear inequalities over the rational and real numbers. This paper investigates various heuristics for optimisation of the method and presents experimental evaluation. The method and heuristics are evaluated against various benchmarks and compared to other methods, such as the FourierMotzkin ...

Local search heuristics are among the most popular approaches to solve hard optimization problems. Among them, Very Large Scale Neighborhood Search techniques present a good balance between the quality of local optima and the time to search a neighborhood. We develop a language to generate exponentially large neighborhoods for sequencing problems using grammars. We develop efficient generic dyn...