Automated planning has been an active area of research in theoretical computer science and Artificial Intelligence (AI) for over 40 years. Planning is the study of general purpose algorithms that accept as input an initial state, a set of desired goal states, and a planning domain model that describes how actions can transform the state. The problem is to find a sequence of actions that transfo...

A Distributed Constraint Satisfaction Problem (DisCSP) is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various algorithms for solving DisCSPs have been developed, which are intended for general purposes, i.e., they can be applied to any network structure. However, if a network has some particular structure, e.g., the network structu...

Hierarchical Reinforcement Learning (HRL) outperforms many ‘flat’ Reinforcement Learning (RL) algorithms in some application domains. However, HRL may need longer time to obtain the optimal policy because of its large action space. Potential Based Reward Shaping (PBRS) has been widely used to incorporate heuristics into flat RL algorithms so as to reduce their exploration. In this paper, we inv...

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have insp...

In previous work, we developed a single Evolutionary Algorithm (EA) to solve random instances of the Anshel-Anshel-Goldfeld (AAG) key exchange protocol over polycyclic groups. The EA consisted six simple heuristics which manipulated strings. present work extends this by exploring use hyper-heuristics in group-theoretic cryptology for first time. Hyper-heuristics are way generate new algorithms ...

In this paper, four algorithms based on some single-pass heuristics are proposed to solve the flexible flow-shop group scheduling problems with more than two machine centers, which have the same number of parallel machines. Single-pass heuristics need less computation time than multiple-pass ones. The single-pass heuristics adopted in the paper includes the PT, the Palmer and the Gupta algorith...

Meta-heuristics are algorithms which are applied to solve problems when conventional algorithms can not find good solutions in reasonable time; evolutionary algorithms are perhaps the most well-known examples of meta-heuristics. As there are many possible meta-heuristics, finding the most suitable meta-heuristic for a given problem is not a trivial task. In order to make this choice, one can de...

In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G∗ of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goalG belongs to G∗ if there is an action ...

Efficiently generating test data is one of the key testing requirements of automated model-based test case generation. Keeping this in mind and driven by the needs of our industrial partners, we propose an improvement in heuristics that we previously defined to generate test data from OCL constraints using search algorithms. We evaluate our improved heuristics using two empirical studies with t...

We consider a version of pattern matching useful in processing large musical data: matching, which consists in finding matches which are -approximate in the sense of the distance measured as maximum difference between symbols. The alphabet is an interval of integers, and the distance between two symbols , is measured as . We also consider -matching, where is a bound on the total sum of the diff...