Autonomous robots are agents that interact with the environment and perform tasks using their own abilities (i.e., skills) without continuous human intervention. However, in real-life scenarios, intelligent robots also need to discover the effects of their actions and understand how to save them for future use. This task appears time-consuming and very challenging, especially in a social environment populated by people who typically modify their behaviors based on the context and can dynamically impact the robot’s decision-making process. This paper aims to investigate the feasibility of autonomously creating an abstract representation of the domain knowledge from the data acquired during the robot’s exploration, inferring causal-effect relations between the executed actions, and learning context-aware symbols that describe the environment states at high level, ultimately producing a PDDL-based description of the domain. With this purpose, a new framework that relies on ROS, the standard de-facto in robotics, and ROSPlan has been developed to facilitate the transfer into several robotic platforms. Preliminary results suggest the possibility of describing the robot’s experience per option via context-based symbols that are consistently learned by the system from a few data samples.

Abstract The gate-based model is one of the leading quantum computing paradigms for representing quantum circuits. Within this paradigm, a quantum algorithm is expressed in terms of a set of quantum gates that are executed on the quantum hardware over time, subject to a number of constraints whose satisfaction must be guaranteed before running the circuit, to allow for feasible execution. The need to guarantee the previous feasibility condition gives rise to the Quantum Circuit Compilation Problem (QCCP). The QCCP has been demonstrated to be NP-Complete, and can be considered as a Planning and Scheduling problem. In this paper, we consider quantum compilation instances deriving from the general Quantum Approximation Optimization Algorithm (QAOA), applied to the MaxCut problem, devised to be executed on Noisy Intermediate Scale Quantum (NISQ) hardware architectures. More specifically, in addition to the basic QCCP version, we also tackle other variants of the same problem such as the QCCP-X (QCCP with crosstalk constraints), the QCCP-V (QCCP with variable qubit state initialization), as well as the QCCP-VX that includes both previous variants. All problem variants are solved using genetic algorithms. We perform an experimental study across a conventional set of instances taken from the literature, and show that the proposed genetic algorithm, termed $$GA_{VX}$$ G A VX , outperforms previous approaches in the literature.

Energy awareness is one of the most relevant research directions in scheduling problems. In this paper we consider the minimization of both the makespan and the energy consumption in the classical job shop scheduling problem. The energy model considered allows several possible states for the machines: off, stand-by, idle, setup and processing. To solve this multi-objective problem we propose an NSGA-II based evolutionary algorithm combined with local search and a heuristic procedure to improve the energy consumption of a given schedule. We also propose an advanced constraint programming (CP) approach as well as a Mixed-Integer Linear Programming (MILP) model, to the aim of comparing their performances against those obtained with the NSGA-II. The experimental study is performed against a benchmark set that extends by 41 instances of increasing size, the set tackled in the previous literature against the same problem. The experiments demonstrate the superiority of the NSGA-II algorithm over all other methods, despite the utilization of CP and MILP allows to draw interesting conclusions on the overall solution optimality, revealing that there is still room for further optimization.

This paper presents the achievements obtained from a study performed within the IMPACT (Intrinsically Motivated Planning Architecture for Curiosity-driven roboTs) Project funded by the European Space Agency (ESA).The main contribution of the work is the realization of an innovative robotic architecture in which the well-known three-layered architectural paradigm (decisional, executive, and functional) for controlling robotic systems is enhanced with autonomous learning capabilities.The architecture is the outcome of the application of an interdisciplinary approach integrating Artificial Intelligence (AI), Autonomous Robotics, and Machine Learning (ML) techniques.In particular, state-of-the-art AI planning systems and algorithms were integrated with Reinforcement Learning (RL) algorithms guided by intrinsic motivations (curiosity, exploration, novelty, and surprise).The aim of this integration was to: (i) develop a software system that allows a robotic platform to autonomously represent in symbolic form the skills autonomously learned through intrinsic motivations; (ii) show that the symbolic representation can be profitably used for automated planning purposes, thus improving the robot's exploration and knowledge acquisition capabilities.The proposed solution is validated in a test scenario inspired by a typical space exploration mission involving a rover.

In this work we investigate the performance of greedy randomised search (GRS) techniques to the problem of compiling quantum circuits to emerging quantum hardware. Quantum computing (QC) represents the next big step towards power consumption minimisation and CPU speed boost in the future of computing machines. Quantum computing uses quantum gates that manipulate multi-valued bits (qubits). A quantum circuit is composed of a number of qubits and a series of quantum gates that operate on those qubits, and whose execution realises a specific quantum algorithm. Current quantum computing technologies limit the qubit interaction distance allowing the execution of gates between adjacent qubits only. This has opened the way to the exploration of possible techniques aimed at guaranteeing nearest-neighbor (NN) compliance in any quantum circuit through the addition of a number of so-called swap gates between adjacent qubits. In addition, technological limitations (decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized. One core contribution of the paper is the definition of two lexicographic ranking functions for quantum gate selection, using two keys: one key acts as a global closure metric to minimise the solution makespan; the second one is a local metric, which favours the mutual approach of the closest qstates pairs. We present a GRS procedure that synthesises NN-compliant quantum circuits realizations, starting from a set of benchmark instances of different size belonging to the Quantum Approximate Optimization Algorithm (QAOA) class tailored for the MaxCut problem. We propose a comparison between the presented meta-heuristics and the approaches used in the recent literature against the same benchmarks, both from the CPU efficiency and from the solution quality standpoint. In particular, we compare our approach against a reference benchmark initially proposed and subsequently expanded in [1] by considering: (i) variable qubit state initialisation and (ii) crosstalk constraints that further restrict parallel gate execution.

Quantum Computing represents the next big step towards speed boost in computation, which promises major breakthroughs in several disciplines including Artificial Intelligence. This paper investigates the performance of a genetic algorithm to optimize the realization (compilation) of nearest-neighbor compliant quantum circuits. Currrent technological limitations (e.g., decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized, and therefore the makespanminimization problem of compiling quantum algorithms on present or future quantum machines is dragging increasing attention in the AI community. In our genetic algorithm, a solution is built utilizing a novel chromosome encoding where each gene controls the iterative selection of a quantum gate to be inserted in the solution, over a lexicographic double-key ranking returned by a heuristic function recently published in the literature.Our algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that our genetic approach obtains very encouraging results that outperform the solutions obtained in previous research against the same benchmark, succeeding in significantly improving the makespan values for a great number of instances.

One of the most recent and interesting trends in intelligent scheduling is trying to reduce the energy consumption in order to obtain lower production costs and smaller carbon footprint. In this work we consider the energy-aware job shop scheduling problem, where we have to minimize at the same tim e an efficiency-based objective, as is the total weighted tardiness, and also the overall energy consumption. We experimentally show that we can reduce the energy consumption of a given schedule by delaying some operations, and to this end we design a heuristic procedure to improve a given schedule. As the problem is computationally complex, we design three approaches to solve it: a Pareto-based multiobjective evolutionary algorithm, which is hybridized with a multiobjective local search method and a linear programming step, a decomposition-based multiobjective evolutionary algorithm hybridized with a single-objective local search method, and finally a constraint programming approach. We perform an extensive experimental study to analyze our algorithms and to compare them with the state of the art.

In this paper, we tackle the Energy-Flexible Flow Shop Scheduling (EnFFS) problem, a multi-objective optimization problem focused on the minimization of both the overall completion time C and the global energy consumption E 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 E 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 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 present some preliminary results obtained against a EnFFS benchmark recently proposed in the literature, showing some initial but appreciable improvements. Moreover, four new instances are presented and experimented upon, which will be hopefully used by the community as a novel benchmark on which to test new multi-objective optimisation research contributions.

This paper presents the mid-term outcome of the Generic Evolutionary Control Knowledge-based mOdule (Gecko) research project, i.e., a layered architecture to implement a cooperative model-based control agent for a Reconfigurable Transportation System (RTSs). A manufacturing plant is here conceived as multiple independent modules to implement alternative inbound logistic systems' configurations. To support this capability of the mechatronic hardware, an integrated solution is proposed using a knowledge-based approach to support a timeline-based planning and control module responsible for managing both the node regular activities and reconfiguration activities. A cooperation layer dedicated to multi-module coordination completes the overall architecture.

Highly automated production systems are conceived to efficiently handle evolving production requirements. This concerns any level of the system from the configuration and control to the management of production. The proposed work deals with the development of an innovative platform jointly managing the production scheduling level and the automation level. The major advantage coming from the platform is the capacity of generating scheduling plans which are executed at automation level and concurrently monitored over time so that any production anomaly or system misbehavior can be dynamically interpreted and adapted by regenerating online a new schedule. The paper describes the current release of our closed loop architecture that integrated both control and automation parts.

Highly reconfigurable and agile production systems are selected to operate in production contexts often characterized by changes of the production requirements or changes of the part family demand. The operational level for such system architecture is expected to manage the short term production planning while guaranteeing the automation layer enables physical devices to exploit logic control tasks within the specific time buckets. The proposed work outlines an integrated approach supporting the operational level for RMSs in which the scheduling of production jobs and the scheduling of corresponding automation tasks are dynamically coupled. Connections with consolidated constraint-based representation and solving techniques are also discussed. The integrated scheduling approach has been validated with reference to a Finishing Robotic Cell (FRC) operating in a pilot assembly line.

This article describes aspects of a fully implemented artificial intelligence (AI) system that integrates multiple intelligent components to actively assist an elderly person at home. Specifically, we describe how constraint‐based scheduling technology is used to actively monitor a pattern of activities executed by the person and how detected temporal constraint violations are used to trigger meaningful and contextualized proactive interactions. This article also presents a psychological evaluation of the system focusing on elderly people's attitudes, in which system acceptability, perceived utility, interaction modality, and emotional response are considered.

The main result of the paper is the reduction of the RCPSP/max problem to a Disjunctive Temporal Problem that allows customization of specific properties within a backtracking search procedure for makespan optimization. In addition, a branching strategy is proposed able to deduce new constraints which explicitly represent infeasible or useless search paths. A new variable ordering heuristic (called clustering) is also used which provides a further boosting to the algorithm's effectiveness.

The high aging society has become a main problem in advanced countries. In this paper common problems of the high aging society in Italy and Japan are described. Then, new healthcare systems and some field trials in both countries are introduced and compared. At last, how to improve the life style of elderly people in Italy and Japan are proposed.