Inspired by probabilistic path planning, we contribute a planning approach that probabilistically balances heuristics and past plans as guidance to planning search. Our ERRT-PLAN algorithm generates multiple search branches probabilistically choosing to extend them towards the current goal or towards actions or goals of a past given plan. We have defined domains to show where current techniques...

Büchi complementation has been studied for five decades since the formalism was introduced in 1960. Known complementation constructions can be classified into Ramsey-based, determinization-based, rank-based, and slice-based approaches. For the performance of these approaches, there have been several complexity analyses but very few experimental results. What especially lacks is a comparative ex...

Complementation of Büchi automata has been studied for over five decades since the formalism was introduced in 1960. Known complementation constructions can be classified into Ramsey-based, determinization-based, rank-based, and slice-based approaches. Regarding the performance of these approaches, there have been several complexity analyses but very few experimental results. What especially la...

Non-Euclidean traveling salesman problem (TSP) construction heuristics, and especially asymmetric TSP construction heuristics, have been neglected in the literature by comparison with the extensive e€orts devoted to studying Euclidean TSP construction heuristics. This state of a€airs is at odds with the fact that asymmetric models are relevant to a wider range of applications, and indeed are un...

This work describes a new way of employing problem-specific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improvements not present in early algorithms are now introduce...

This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on so...

The goal of this thesis is to extend Answer Set Programming (ASP) with declarative heuristics, preferences, and online planning capabilities. For declarative heuristics, the thesis presents a general declarative approach for incorporating domain-specific heuristics into ASP solving by means of logic programming rules. For preferences, the approach developed in my thesis and the resulting asprin...

Nurse scheduling is a difficult optimization problem with multiple constraints. There is extensive research in the literature solving the problem using meta-heuristics approaches. In this paper, we will investigate an intelligent search heuristics that handles cost based scheduling problem. The heuristics demonstrated superior performances compared to the original algorithms used to solve the p...

This thesis aims at exploring the potential of heuristic search algorithms for Abstract Argumentation. Therefore specific backtracking search algorithms are presented, which support the use of heuristics. Thereafter several heuristics are defined which have been implemented as part of this thesis. These will then be experimentally compared among each other and with other approaches to Abstract ...