Many applications from planning and scheduling to problems in molecular biology rely heavily on a temporal reasoning component In this paper we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen s in uential interval based framework for representing temporal information At the core of the system are algorithms for determining whether the tempo...

Most state of the art SAT solvers for industrial problems are based on the Conflict Driven Clause Learning (CDCL) paradigm. Although this paradigm evolved from the systematic DPLL search algorithm, modern techniques of far backtracking and restarts make CDCL solvers non-systematic. CDCL solvers do not systematically examine all possible truth assignments as does DPLL. Local search solvers are a...

This work investigates and develops a backtracking algorithm with a novel approach to enumerating and traversing search space. A basic logical framework inspired by relevant logics is presented, highlighting relationships between search and refutation proof construction. Mechanisation of a relevance aware Davis Putnam Logemann Loveland procedure is investigated, and this yields an intelligent b...

In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Arti cial Intelligence can be formalized as distributed CSPs. We prese...

Many important problems in Artificial Intelligence can be defined as Constraint Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constraints on the values of those variables (these problems are also called Consistent Labeling Problems (CLP), in which “Labeling means assigning a value to a variable.) Solu...

This paper deals with the application of Iterative Learning Control (ILC) to further improve the performance of teleoperation systems based on Smith predictor. The goal is to achieve robust stability and optimal transparency for these systems. The proposed control structure make the slave manipulator follow the master in spite of uncertainties in time delay in communication channel and model pa...

We propose an algorithm for nonlinear optimization that employs both trust region techniques and line searches Unlike traditional trust region methods our algorithm does not resolve the subproblem if the trial step results in an increase in the objective function but instead performs a backtracking line search from the failed point Backtracking can be done along a straight line or along a curve...

In this paper, we propose a novel search algorithm called band search that generalizes guided depth-first and best-first searches. The search allocates a band of at most W nodes in each level of the search tree for storing active nodes in that level, D priority lists, one for each level, for storing overflow nodes, and D counters, one for each level, for keeping track of and to limit the degree...

One of the most powerful techniques for solving centralized constraint satisfaction problems (CSPs) consists of maintaining local consistency during backtrack search (e.g. [11]). Yet, no work has been reported on such a combination in asynchronous settings. The difficulty in this case is that, in the usual algorithms, the instantiation and consistency enforcement steps must alternate sequential...