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ISSN 1724-8035 (P)
ISSN 2211-0097 (E)
Intelligenza Artificiale is the official journal of the Italian Association for Artificial Intelligence (AI*IA). Intelligenza Artificiale publishes rigorously reviewed articles (in English) in all areas of Artificial Intelligence, with a special attention to original contributions. It will also publish assessments of the state of the art in various areas of AI, and innovative system descriptions with appropriate evaluation.
The Editor-in-Chief welcomes proposals for special issues, book reviews, conference reports and news items of interest to the AI research community. Intelligenza Artificiale is an international journal and welcomes submissions from every country.
Abstract: The more practical and interesting versions of the permutation flowshop scheduling problem (PFSP) have a variety of objective criteria to be optimized simultaneously. Multi-objective PFSP is also a relevant combinatorial multi-objective optimization problem. In this paper we propose a multi-objective evolutionary algorithm for PFSP by extending the previously proposed discrete differential evolution (DE) scheme for single-objective PFSP. This is the first application of the algebraic-based discrete DE to multi-objective problems. The algorithm is extended by adopting a variety of crossover and multi-objective selection operators. Among these, the multi-objective α -selection is a novelty of this work and can be decoupled…from DE and used also in other evolutionary algorithms. The other crossover and selection operators have been taken from the existing literature and, where required, have been adapted to the problem at hand. An experimental evaluation has been conducted on all the three bi-objective PFSPs among the makespan, total flowtime and total tardiness criteria. The results show that the proposed approach is competitive with respect to the state-of-the-art algorithms.
Abstract: Domain-Independent planning is known to be a very hard search problem, and in the last three decades many search techniques and heuristics have been developed with the aim of efficiently solving such a task. These techniques and heuristics include the usage of landmarks, which are logical expressions consisting of facts that become true or actions that are executed in any solution plan. We propose the usage of landmarks for speeding up the search of the planner LPG , a system implementing a planning approach based on the use of local search in the space of the action graphs of the…planning problem. The results of an experimental evaluation of the proposed techniques show that these techniques can improve the performance of LPG , obtaining a planning system that performs similarly to the state-of-the-art planner LAMA. Moreover, we introduce and experimentally evaluate the concept of “quasi-landmarks”; these are facts that are likely to become true in every solution plan, or facts that must become true in a subset of the solution plans.
Keywords: AI planning, local search, heuristic search, landmarks techniques
Abstract: The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. These trends are occurring in the context of concerns around environmental issues of poor air quality and transport related carbon dioxide emissions. One out of several ways to help meet these challenges is in the intelligent routing of road traffic through congested urban areas. Our goal is to show the feasibility of using automated planning to perform this routing, taking into account a knowledge of vehicle types, vehicle emissions, route maps, air quality zones, etc. Specifically focusing on…air quality concerns, in this paper we investigate the problem where the goals are to minimise overall vehicle delay while utilising network capacity fully, and respecting air quality limits. We introduce an automated planning approach for the routing of traffic to address these areas. The approach has been evaluated on micro-simulation models that use real-world data supplied by our industrial partner. Results show the feasibility of using AI planning technology to deliver efficient routes for vehicles that avoid the breaking of air quality limits, and that balance traffic flow through the network.
Abstract: Taking inspiration from both Constraint Programming (CP) and Logic Programming (LP), the I LO C domain-independent planning system is a timeline-based planner that allows to model both planning and scheduling problems according to a uniform schema. This paper presents a complete description of the planner and describes two domain independent heuristics aiming at improving the planner ability to solve classical planning problems. An experimental evaluation demonstrates the I LO C strength in solving temporally expressive problems and its improved ability to address the causal reasoning capability which is a dominant feature of classical planning. Furthermore the experimentation produces some observations…for new directions to synthesize effective general purpose solving abilities.
Abstract: In this paper, we tackle the Energy-Flexible Flow Shop Scheduling (EnFFS) problem, a multi-objective optimisation problem focused on the minimisation of both the overall completion time and the global energy consumption of the solutions. The tackled problem is an extension of the Flexible Flow-Shop Scheduling problem where each activity in a job has a set of possible execution modes with different trade-off between energy consumed and processing time. Moreover, global energy consumption may also depend on the possibility to switch-off the machines during the idle periods. The goal of this work is to widen the knowledge about performance capabilities,…in particular the ability of efficiently finding high quality approximations of the solution Pareto front. To this aim, we explore the development of innovative meta-heuristic algorithms for solving the proposed multi-objective scheduling problem. In particular, we consider a stochastic local search (SLS) algorithms, introducing a Multi-Objective Large Neighbourhood Search (MO-LNS) framework in line with the large neighbourhood search approaches proposed in literature, and compare it with a state-of-the-art Constraint Programming solver. We present some results obtained against both a EnFFS benchmark recently proposed in the literature, and a set of new challenging instances of increasing size.
Keywords: Scheduling, multi-objective optimisation, energy consumption, large neighbourhood search, constraint-based reasoning
Abstract: Several issues in transferring AI results in crowd modeling and simulation are due to the fact that control applications are aimed at achieving optimal solutions, whereas simulations have to deal with the notions of plausibility and validity . The latter requires empirical evidences that, for some specific phenomena, are still scarce and hard to acquire. To face this issue, the present work presents an investigation on the route choice decisions of pedestrians, by producing empirical evidences with an experiment executed in a controlled setting. The experiment involves human participants facing a relatively simple choice among different paths (i.e.…choose one of two available gateways leading to the same target area) in which, however, they face a trade-off situation between length of the trajectory to be covered and estimated travel time, considering the level of congestion in the different paths. The data achieved with the experiment are used to design and evaluate a general simulation model for pedestrian route choice. The proposed model firstly considers the fact that other pedestrians are generally perceived as repulsive and that choice of route is generally aimed at avoiding congestion (as for proxemics theory). On the other hand, we also introduce an additional mechanism due to the conjecture that the decision of a pedestrian to reconsider the adopted path is a locally perceivable event that is able to trigger a similar reconsideration by nearby pedestrians, that can imitate the former one. The model is experimented and evaluated in the experiment scenario, for calibration and validation, as well as in a larger scale environment, for exploring the implications of the modeling choices in a more complex situation.
Keywords: Agent-based systems, modeling and simulation, pedestrian dynamics, route