Learning Classical Planning Strategies with Policy Gradient

A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the approaches. This enables using policy gradient to learn search strategies tailored to a specific distributions of planning problems and a selected performance metric, e.g. the IPC score. We instantiate the framework by constructing a policy space consisting of five search approaches and a two-dimensional representation of the planner's state. Then, we train the system on randomly generated problems from five IPC domains using three different performance metrics. Our experimental results show that the learner is able to discover domain-specific search strategies, improving the planner's performance relative to the baselines of plain best-first search and a uniform policy.Comment: Accepted for ICAPS 201

Learning Weak Constraints in Answer Set Programming

This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.Comment: To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of ICLP 201

Using Event Calculus to Formalise Policy Specification and Analysis

As the interest in using policy-based approaches for systems management grows, it is becoming increasingly important to develop methods for performing analysis and refinement of policy specifications. Although this is an area that researchers have devoted some attention to, none of the proposed solutions address the issues of analysing specifications that combine authorisation and management policies; analysing policy specifications that contain constraints on the applicability of the policies; and performing a priori analysis of the specification that will both detect the presence of inconsistencies and explain the situations in which the conflict will occur. We present a method for transforming both policy and system behaviour specifications into a formal notation that is based on event calculus. Additionally it describes how this formalism can be used in conjunction with abductive reasoning techniques to perform a priori analysis of policy specifications for the various conflict types identified in the literature. Finally, it presents some initial thoughts on how this notation and analysis technique could be used to perform policy refinement

Le città bombardate: un percorso su fonti d’archivio, diari e testimonianze. Un’esperienza di storia sul territorio

Il Laboratorio per la ricerca e la didattica della storia, costituito nel 2002 presso il CIRD dell’Università degli Studi di Udine, negli ultimi anni ha costituito per gli insegnanti delle scuole secondarie della città un luogo di sperimentazione della didattica della storia del Novecento. Attraverso un rapporto collaborativo con i docenti dell’Università, essi hanno cercato nuovi modi per promuovere la didattica della storia. Insegnare storia contemporanea in una regione di frontiera è un tema che assume una connotazione rilevante, poiché i bombardamenti sulle città, nella Seconda guerra mondiale, in una regione di confine, hanno provocato macerie materiali che si sono aggiunte alle macerie morali. Insegnare la storia di luoghi di frontiera, caratterizzati dalla presenza di popolazioni che hanno lingue, culture e tradizioni diverse, consente di mostrare agli studenti come sia importante guardare alle vicende storiche con un “doppio sguardo”

Towards learning domain-independent planning heuristics

Automated planning remains one of the most general paradigms in Artificial Intelligence, providing means of solving problems coming from a wide variety of domains. One of the key factors restricting the applicability of planning is its computational complexity resulting from exponentially large search spaces. Heuristic approaches are necessary to solve all but the simplest problems. In this work, we explore the possibility of obtaining domain-independent heuristic functions using machine learning. This is a part of a wider research program whose objective is to improve practical applicability of planning in systems for which the planning domains evolve at run time. The challenge is therefore the learning of (corrections of) domain-independent heuristics that can be reused across different planning domains.Comment: Accepted for the IJCAI-17 Workshop on Architectures for Generality and Autonom

Quantitative risk assessment on a hydrogen refuelling station

The Directive 2014/94/UE (DAFI, Alternative Fuel Initiative Directive) on the deployment of alternative fuels (i.e. hydrogen) infrastructures has been recently transposed into national law in Italy. Consequently, the technical regulation on fire prevention for H2fuelling stations has been updated, in order to consider the current maximum delivery pressure (700 bar) of gaseous hydrogen for road vehicles. This technical regulation establishes the prescriptive safety distance from a piece of equipment. In the case of a new station, an assessment of the frequency of the event and its potential consequences is necessary. This is to understand which risk can reasonably be mitigated by a safety distance or whether additional mitigation or prevention measures should be taken. This paper presents the quantitative risk assessment (QRA) study on a hydrogen station planned to be installed, study which aims at determining the safety distances. Such study utilizes the Sandia-developed QRA tool, Hydrogen Risk Analysis Model (HyRAM), to calculate risk values when developing risk-equivalent plans. HyRAM combines reduced order deterministic models that characterize hydrogen release and flame behavior with probabilistic risk models to quantify risk values. Thanks to HyRAM tool it is possible to estimate physical effects and consequences on people and structures and plants, related to risk scenarios, by means of a damage model library. Use of risk assessment may allow station owners and designers to flexibly define station-specific mitigations, with the purpose of achieving equal or better levels of safety with respect to prescriptive recommendation levels, as suggested by ISO19880-1 (2018)