The Maximum Diversity Problem (MDP) consists in selecting a subset of m elements from a given set of n elements (n > m) in such a way that the sum of the pairwise distances between the m chosen elements is maximized. We present a hybrid metaheuristic algorithm (denoted by MAMDP) for MDP. The algorithm uses a dedicated crossover operator to generate new solutions and a constrained neighborhood t...

Unexpected findings on bone scintigraphy such as asymmetrical uptake in extremities may cause confusion for the diagnosis. The authors describe three cases of accidental intraarterial injection of Tcmethylenediphosphonate (Tc-MDP)on theantecubital regionanddiscuss thefindings anddifferential diagnosis. © 2010 Elsevier España, S.L. and SEMNIM. All rights reserved. Aumento asimétrico de la captac...

BACKGROUND Dyspnea is among the most common reasons for emergency department (ED) visits by patients with cardiopulmonary disease who are commonly asked to recall the symptoms that prompted them to come to the ED. The reliability of recalled dyspnea has not been systematically investigated in ED patients. METHODS Patients with chronic or acute cardiopulmonary conditions who came to the ED wit...

Synthetic muramyl dipeptide (MDP) could stimulate skin fibroblasts of the guinea pig to produce thymocyte-activating factor, which augments the proliferative response of thymocytes to phytohemagglutinin (PHA). Adjuvant-active analogues of MDP also stimulated fibroblasts to produce the factor, whereas adjuvant-inactive analogues failed to do so. Thus a marked parallelism was found between adjuva...

This paper examines a number of solution methods for decision processes with non-Markovian rewards (NMRDPs). They all exploit a temporal logic specification of the reward function to automatically translate the NMRDP into an equivalent Markov decision process (MDP) amenable to well-known MDP solution methods. They differ however in the representation of the target MDP and the class of MDP solut...

A popular approach to solving a decision process with non-Markovian rewards (NMRDP) is to exploit a compact representation of the reward function to automatically translate the NMRDP into an equivalent Markov decision process (MDP) amenable to our favorite MDP solution method. The contribution of this paper is a representation of non-Markovian reward functions and a translation into MDP aimed a...

PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm performs near optimally for all but a polynomial number of steps. The performance of these algorithms can be further improved by incorporating domain knowledge to guide their learning process. In this paper we propose a ...

RÉSUMÉ. Le modèle des processus décisionnels de Markov (MDP) offre un cadre général pour la résolution de problèmes de décision séquentielle dans l’incertain. Son exploitation suppose une connaissance précise des valeurs des paramètres (probabilités et récompenses). Dans ce papier, les récompenses sont qualitatives ou ne sont connues que de manière imparfaite. Seul un ordre est supposé connu. U...

One of the most widely used methods for solving average cost MDP problems is the value iteration method. This method, however, is often computationally impractical and restricted in size of solvable MDP problems. We propose acceleration operators that improve the performance of the value iteration for average reward MDP models. These operators are based on two important properties of Markovian ...

T paper presents a new randomized search method called evolutionary random policy search (ERPS) for solving infinite-horizon discounted-cost Markov-decision-process (MDP) problems. The algorithm is particularly targeted at problems with large or uncountable action spaces. ERPS approaches a given MDP by iteratively dividing it into a sequence of smaller, random, sub-MDP problems based on informa...