Abstract Motion-based technology (MBT) has been applied in the last decades with enormous success in a high number of applications. Its use continues growing and is specially interesting in the health area. Nowadays, its employment is being more and more specialised with respect to the profile of the end user (i.e., child, adolescent/teenager, adult or elderly). This paper first reviews the use of MBT centered in the end user from a global perspective. It also proposes a taxonomy that allows cataloguing the MBT employment directed to the end user. Then, from these results, the paper centers the review on the MBT application aiming to improve the health of elderly. The results highlighted in this paper can help to a better understanding of MBT, especially when it is applied thinking in elderly as the end users.

The classical approach of competitive coevolution (CC) applied in games tries to exploit an arms race between coevolving populations that belong to the same species (or at least to the same biotic niche), namely strategies, rules, tracks for racing, or any other. This paper proposes the coevolution of entities belonging to different realms (namely biotic and abiotic) via a competitive approach. More precisely, we aim to coevolutionarily optimize both virtual players and game content. From a general perspective, our proposal can be viewed as a method of procedural content generation combined with a technique for generating game AI. This approach can not only help game designers in game creation but also generate content personalized to both specific players' profiles and game designer's objectives (e.g., create content that favors novice players over skillful players). As a case study we use Planet Wars, the real-time strategy (RTS) game associated with the 2010 Google AI Challenge contest, and demonstrate (via an empirical study) the validity of our approach.

Procedural content generation (PCG) is a research field on the rise, with numerous papers devoted to this topic. This paper presents a PCG method based on a self-adaptive evolution strategy for the automatic generation of maps for the real-time strategy (RTS) game Planet Wars. These maps are generated in order to fulfill the aesthetic preferences of the user, as implied by her assessment of a collection of maps used as training set A topological approach is used for the characterization of the maps and their subsequent evaluation: the sphere-of-influence graph (SIG) of each map is built, several graph-theoretic measures are computed on it, and a feature selection method is utilized to determine adequate subsets of measures to capture the class of the map. A multiobjective evolutionary algorithm is subsequently employed to evolve maps, using these feature sets in order to measure distance to good (aesthetic) and bad (non-aesthetic) maps in the training set. The so-obtained results are visually analyzed and compared to the target maps using a Kohonen network.

A weighted constraint satisfaction problem (WCSP) is a constraint satisfaction problem in which preferences among solutions can be expressed. Bucket elimination is a complete technique commonly used to solve this kind of constraint satisfaction problem. When the memory required to apply bucket elimination is too high, a heuristic method based on it (denominated mini-buckets) can be used to calculate bounds for the optimal solution. Nevertheless, the curse of dimensionality makes these techniques impractical on large scale problems. In response to this situation, we present a memetic algorithm for WCSPs in which bucket elimination is used as a mechanism for recombining solutions, providing the best possible child from the parental set. Subsequently, a multi-level model in which this exact/metaheuristic hybrid is further hybridized with branch-and-bound techniques and mini-buckets is studied. As a case study, we have applied these algorithms to the resolution of the maximum density still life problem, a hard constraint optimization problem based on Conway's game of life. The resulting algorithm consistently finds optimal patterns for up to date solved instances in less time than current approaches. Moreover, it is shown that this proposal provides new best known solutions for very large instances.

Abstract This paper presents a computational model for the cooperation of constraint domains and an implementation for a particular case of practical importance. The computational model supports declarative programming with lazy and possibly higher-order functions, predicates, and the cooperation of different constraint domains equipped with their respective solvers, relying on a so-called constraint functional logic programming (CFLP) scheme. The implementation has been developed on top of theCFLPsystem , supporting the cooperation of the three domains ℋ, ℛ, and ℱ , which supply equality and disequality constraints over symbolic terms, arithmetic constraints over the real numbers, and finite domain constraints over the integers, respectively. The computational model has been proved sound and complete w.r.t. the declarative semantics provided by theCFLPscheme, while the implemented system has been tested with a set of benchmarks and shown to behave quite efficiently in comparison to the closest related approach we are aware of.

This paper presents a computational model for the cooperation of constraint domains, based on a generic Constraint Functional Logic Programming (CFLP) Scheme and designed to support declarative programming with functions, predicates and the cooperation of different constraint domains equipped with their respective solvers. We have developed an implementation in the CFLP system TOY, supporting an instance of the scheme which enables the cooperation of symbolic Herbrand constraints, finite domain integer constraints, and real arithmetic constraints. We provide a theoretical result and an analysis of benchmarks showing a good performance with respect to the closest related approach we are aware of

Finding binary sequences with low auto correlation is a very hard problem with many practical applications. In this paper we analyze several meta heuristic approaches to tackle the construction of this kind of sequences. We focus on two different local search strategies, steepest descent local search (SDLS) and tabu search (TS), and their use both as stand-alone techniques and embedded within a memetic algorithm (MA). Plain evolutionary algorithms are shown to perform worse than stand-alone local search strategies. However, a MA endowed with TS turns out to be a state-of-the-art algorithm: it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature.

Abstract In this paper, we present our proposal to Constraint Functional Logic Programming over Finite Domains (CFLP( $\fd$ )) with a lazy functional logic programming language which seamlessly embodies finite domain ( $\fd$ ) constraints. This proposal increases the expressiveness and power of constraint logic programming over finite domains (CLP( $\fd$ )) by combining functional and relational notation, curried expressions, higher-order functions, patterns, partial applications, non-determinism, lazy evaluation, logical variables, types, domain variables, constraint composition, and finite domain constraints. We describe the syntax of the language, its type discipline, and its declarative and operational semantics. We also describe \toy(fd)$ , an implementation forCFLP( $\fd$ ), and a comparison of our approach with respect toCLP( $\fd$ ) from a programming point of view, showing the new features we introduce. And, finally, we show a performance analysis which demonstrates that our implementation is competitive with respect to existingCLP( $\fd$ ) systems and that clearly outperforms the closer approach toCFLP( $\fd$ ).

Branch-and-bound (BnB) and memetic algorithms represent two very different approaches for tackling combinatorial optimization problems. However, these approaches are compatible. In this correspondence, a hybrid model that combines these two techniques is considered. To be precise, it is based on the interleaved execution of both approaches. Since the requirements of time and memory in BnB techniques are generally conflicting, a truncated exact search, namely, beam search, has opted to be carried out. Therefore, the resulting hybrid algorithm has a heuristic nature. The multidimensional 0-1 knapsack problem and the shortest common supersequence problem have been chosen as benchmarks. As will be shown, the hybrid algorithm can produce better results in both problems at the same computational cost, especially for large problem instances.

Branch-and-bound and evolutionary algorithms represent two very different approaches for tackling combinatorial optimization problems. These approaches are not incompatible though. In this paper, we consider a hybrid model that combines these two techniques. To be precise, it is based on the interleaved execution of both approaches, and has a heuristic nature. The multidimensional 0-1 knapsack problem has been chosen as benchmark. As it is shown, the hybrid algorithm can produce better results at the same computational cost, especially for larger problem instances.

We present a generic framework for defining and solving interval constraints on any set of domains (finite or infinite) that are lattices. The approach is based on the use of a single form of constraint similar to that of an indexical used by CLP for finite domains and on a particular generic definition of an interval domain built from an arbitrary lattice. We provide the theoretical foundations for this framework and a schematic procedure for the operational semantics. Examples are provided that illustrate how new (compound) constraint solvers can be constructed from existing solvers using lattice combinators and how different solvers (possibly on distinct domains) can communicate and hence, cooperate in solving a problem. We describe the language clp ( L ), which is a prototype implementation of this framework and discuss ways in which this implementation may be improved.