In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to create a Planning Domain Definition Language (PDDL) representation, which is the most widely used input format for most off-the-shelf automated planners, starting from `options', a data structure used to represent actions within the hierarchical reinforcement learning framework. We propose an architecture that potentially removes the need for human intervention. In particular, the architecture first acquires options in a fully autonomous fashion on the basis of open-ended learning, then builds a PDDL domain based on symbols and operators that can be used to accomplish user-defined goals through a standard PDDL planner. We start from an implementation of the above mentioned procedure tested on a set of benchmark domains in which a humanoid robot can change the state of some objects through direct interaction with the environment. We then investigate some critical aspects of the information abstraction process that have been observed, and propose an extension that mitigates such criticalities, in particular by analysing the type of classifiers that allow a suitable grounding of symbols.

Energy costs are an increasingly important issue in real-world scheduling, for both economic and environmental reasons. This paper deals with a variant of the well-known job shop scheduling problem, where we consider a bi-objective optimization of both the weighted tardiness and the energy costs. To this end, we design a hybrid metaheuristic that combines a genetic algorithm with a novel local search method and a linear programming approach. We also propose an efficient procedure for improving the energy cost of a given schedule. In the experimental study we analyse our proposal and compare it with the state of the art and also with a constraint programming approach, obtaining competitive results.

The Disjunctive Temporal Problem (DTP) involves the satisfaction of aset of constraints represented by disjunctive formulas of the form x 1 - y 1 <= r 1 or x 2 - y 2 <= r 2 or ... or x k - y k <= r k . DTP is a  general temporal reasoning problem which includes the well-known Temporal Constraint Satisfaction Problem (TCSP) introduced by Dechter, Meiri and Pearl. This paper describes a basic constraint satisfaction algorithm where several aspects of the current literature are integrated, in particular the so-called incremental forward checking.  Hence,two new extended solving strategies are proposed and experimentally evaluated. The new proposed strategies are very competitive with the best results available in the current literature. In addition, the analysis of the empirical results suggests future research directions concerning in particular the use of arc-consistency filtering strategies.

This paper proposes an iterative improvement approach for solving the Resource Constraint Project Scheduling Problem with Time-Windows (RCPSP/max), a well-known and challenging NP-hard scheduling problem. The algorithm is based on Iterative Flattening Search (IFS), an effective heuristic strategy for solving multi-capacity optimization scheduling problems. Given an initial solution, IFS iteratively performs two-steps: a relaxation-step , that randomly removes a subset of solution constraints and a solving-step , that incrementally recomputes a new solution. At the end, the best solution found is returned. The main contribution of this paper is the extension to RCPSP/max of the IFS optimization procedures developed for solving scheduling problems without time-windows. An experimental evaluation performed on medium-large size and web-available benchmark sets confirms the effectiveness of the proposed procedures. In particular, we have improved the average quality w.r.t. the current bests, while discovering three new optimal solutions, thus demonstrating the general efficacy of IFS.