In this paper a model and algorithmic solution are created to control the path planning decision processes of multiple cooperating autonomous aerial vehicles engaged in a search of an uncertain and risky environment. Methods to incorporate a priori and dynamic information about the targets and to incorporate threats in a three-dimensional environment into a computationally feasible dynamic prog...

This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of Bayesian Networks with the expressive power of higher-order logics. We discuss how the proposed graphical model is used in order to define a probability distribution semantics over particular families of higher-order terms. We give an example of the appl...

MOTIVATION Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. RESULTS We evaluate Hidden Markov Model structures for bacter...

Hybrid probabilistic programs framework [5] is a variation of probabilistic annotated logic programming approach, which allows the user to explicitly encode the available knowledge about the dependency among the events in the program. In this paper, we extend the language of hybrid probabilistic programs by allowing disjunctive composition functions to be associated with heads of clauses and ch...

We survey three applications of mathematical programming to rea soning under uncertainty a an application of linear programming to probabilistic logic b an application of nonlinear programming to Bayesian logic a combination of Bayesian inference with probabilistic logic and c an application of integer programming to Dempster Shafer theory which is a method of combining evidence from di erent s...

We see reversible computing as a generalisation of sequential computation obtained by revoking the law of the excluded miracle. Our execution language includes naked guarded commands and non-deterministic choice. Choices which lead to miraculous continuations invoke reverse computation, and non-deterministic choice plays the rôle of provisional choice within a backtracking context. We require p...

Probabilistic Inductive Logic Programming (PILP) is gaining interest due to its ability to model domains with complex and uncertain relations among entities. Since PILP systems generally must solve a large number of inference problems in order to perform learning, they rely critically on the support of efficient inference systems. PITA [7] is a system for reasoning under uncertainty on logic pr...

A probabilistic constrained stochastic programming model is formulated, where one term in the objective function, to be minimized, is the maximum of a finite or infinite number of linear functions. The model is reformulated as a finite or semiinfinite disjunctive programming problem. Duality relationships are established for both the original and the convexified problems. Numerical solution tec...

There has been a surge of research interest in Estimation of Distribution Algorithms (EDA). Several reviews on current work in conventional EDA are available. Although most work has focused on one dimensional representations that resembles the chromosomes of Genetic Algorithms (GA), an interesting stream of EDA using more complex tree representations has recently received some attention. To dat...