A new search algorithm for solving distributed constraint optimization (DCOP) problems is presented. The proposed algorithm performs concurrent search on non intersecting parts of the global search space, using multiple search processes. Each Search Process uses synchronous forward bounding in its sub-space, to prune the remaining search space. Concurrent Forward Bounding (ConcFB) dynamically s...

Methods for protein structure (3D)-sequence (1D) compatibility evaluation (threading) have been developed during the past decade. The protocol in which a sequence can recognize its compatible structure in the structural library (i.e., the fold recognition or the forward-folding search) is available for the structure prediction of new proteins. However, the reverse protocol, in which a structure...

This paper defines and studies a variable selection procedure called Testing-Based Forward Model Selection. The procedure inductively selects covariates which increase predictive accuracy into a working statistical regression model until a stopping criterion is met. The stopping criteria and selection criteria are defined using statistical hypothesis tests. The paper explicitly describes a test...

This study examines the influence of the fourth generation quarks on the double lepton polarizations forward–backward asymmetries in B → K * ℓ + ℓ − decay. We obtain that for both (µ, τ) channels the magnitude and the sign of the differential forward–backward asymmetries and the magnitude of the average forward–backward asymmetries are quite sensitive to the 4th generation quarks mass and mixin...

In this paper, we present a comprehensive, yet simple, extension to the existing systems used for Digital Contact Tracing in Covid-19 pandemic. The extension, called BubbleAntiCovid19 (BAC19), enables those systems, regardless of their underlying protocol, enhance sets traced contacts and improve global fight against pandemic during phase opening borders enabling more traveling. BAC19 is Struct...

Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or “simulator”) of the game at hand. However, in some games such forward model is not readily available. In this paper we address the problem of automatically learning forward models (more specifically, combats models) for two-player attrition games. We report experiments comparing several ap...

In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. These cost vectors can be partially or completely ordered using a preference relation compatible with Pareto dominance. In this context, multiobjective preference-based search (MOPBS) aims at computing the preferred feasible solutions according to a predefined preference model, these preferred soluti...

Recently tremendous advances have been made in the performance of AI planning systems. However increased performance is only one of the prerequisites for bringing planning into the realm of real applications; advances in the scope of problems that can be represented and solved must also be made. In this paper we address two important representational features, concurrently executable actions wi...

In Interactive Storytelling (IS) the prevailing approach for the automatic generation of plausible narratives that meet global author goals is intentional planning. However, existing approaches suffer from limited expressiveness and poor scalability. We address this by replacing single intentional planners with multiple agents representing the characters of a narrative, which can reason about t...

In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. These cost vectors can be partially or completely ordered using a preference relation compatible with Pareto dominance. In this context, multiobjective preference-based search (MOPBS) aims at computing the preferred feasible solutions according to a predefined preference model, these preferred soluti...