Early artificial intelligence programming languages that incorporated search and backtracking facilities did not limit backtracking to previous computations when a portion of a search failed [1]. This had two unpleasant consequences: (1) the amount of data that had to be retained to allow backtracking limited the problems that could be handled, and (2) unexpected failure could cause backtrackin...

Lazy Annotation is a method of software model checking that performs a backtracking search for a symbolic counterexample. When the search backtracks, the program is annotated with a learned fact that constrains future search. In this sense, the method is closely analogous to conflictdriven clause learning in SAT solvers. In this paper, we develop several improvements to the basic Lazy Annotatio...

The gradient method is a very efficient iterative technique for solving unconstrained optimization problems. Motivated by recent modifications of some variants the SM method, this study proposed two methods that are globally convergent as well computationally efficient. Each under influence backtracking line search. Results obtained from numerical implementation these and performance profiling ...

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm essential for analyzing path planning. However, current applied to robots still has some limitations, including early blind search, slow convergence speed, more turns. To overcome these problems, improved proposed in this paper. In algorithm, we introduce idea triangle inequality a pseudo-rand...

The performance of a new heuristic search algorithm is analyzed. The algorithm uses a formal representation (semantic representation) that contains enough information to compute the heuristic evaluation function h (n). as defined in the context of A *. without requiring a human expert to provide it. The heuristic is computed by �olving less constrained subproblems (auxiliary problems) of the gi...

A heuristic technique that combines a genetic algorithm with a Tabu Search algorithm is applied to the Quadratic Assignment Problem (QAP). The hybrid algorithm improves the results obtained through the application of each of these algorithms separately. The QAP is a NP-hard problem and instances of size n > 15 are still considered intractable. The results of our experiments suggest that CHC com...

This work examines the problem of searching for schedulable real-time control policies for resource-limited agents acting in dynamic environments. The dynamic properties of the environment and resource limitations of the agent render the problem of solving for an optimal policy infeasible. We therefore limit our search to a satisficing, rather than an optimal policy. We view the policy search a...

This paper presents a graph-based backtracking algorithm designed to support constraintbased planning in data production domains. This algorithm performs backtracking at two nested levels: the outerbacktracking following the structure of the planning graph to select planner subgoals and actions to achieve them and the inner-backtracking inside a subproblem associated with a selected action to f...

The finite model generation problem in the first-order logic is a generalization of the propositional satisfiability (SAT) problem. An essential algorithm for solving the problem is backtracking search. In this paper, we show how to improve such a search procedure by lemma learning. For efficiency reasons, we represent the lemmas by propositional formulas and use a SAT solver to perform the nec...