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

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

Technical Communications of ICLP

Abstract The need for systematic research into behavioural factors of individual terrorists has been highlighted by much recent work on terrorism. Many existing methods follow a hypothesistesting approach in which statistical modelling and analysis of existing data is conducted to either confirm or refute a hypothesis. However, the initial construction of hypotheses is not trivial, nor is the decision upon which of the variables are to be considered relevant for the testings. It has been argued that the lack of a methodical approach to represent, analyse, interpret and infer from existing data presents a pressing challenge to the progress of lone-actor terrorism research in particular, and the terrorism field more generally. This paper sets a new agenda for such research. We propose the use of a logic programming approach to address the shortcomings of existing methodologies in the study of lone-actor terrorism. Our method is based on transforming characteristic and behavioural codes into a logic program and applying inductive logic programming to learn hypotheses about potentially relevant factors associated with terrorist behaviour, as well as the influence of specific factors on such behaviour. This paper is an exploratory study of 111 lone-actor terrorists' target selections (civilian vs. high-value targets) and the agency of their ideological orientation in determining their target choices

An Inductive Approach for Modal Transition System Refinement

Modal Transition Systems (MTSs) provide an appropriate framework for modelling software behaviour when only a partial specification is available. A key characteristic of an MTS is that it explicitly models events that a system is required to provide and is proscribed from exhibiting, and those for which no specification is available, called maybe events. Incremental elaboration of maybe events into either required or proscribed events can be seen as a process of MTS refinement, resulting from extending a given partial specification with more information about the system behaviour. This paper focuses on providing automated support for computing strong refinements of an MTS with respect to event traces that describe required and proscribed behaviours using a non-monotonic inductive logic programming technique. A real case study is used to illustrate the practical application of the approach

RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19 Assessment in Primary Care

Clinical decision-making is a fundamental stage in delivering appropriate care to patients. In recent years several decision-making systems designed to aid the clinician in this process have been developed. However, technical solutions currently in use are based on simple regression models and are only able to take into account simple pre-defined multiple-choice features, such as patient age, pre-existing conditions, smoker status, etc. One particular source of patient data, that available decision-making systems are incapable of processing is the collection of patient consultation GP notes. These contain crucial signs and symptoms - the information used by clinicians in order to make a final decision and direct the patient to the appropriate care. Extracting information from GP notes is a technically challenging problem, as they tend to include abbreviations, typos, and incomplete sentences. This paper addresses this open challenge. We present a framework that performs knowledge graph construction from raw GP medical notes written during or after patient consultations. By relying on support phrases mined from the SNOMED ontology, as well as predefined supported facts from values used in the RECAP (REmote COVID-19 Assessment in Primary Care) patient risk prediction tool, our graph generative framework is able to extract structured knowledge graphs from the highly unstructured and inconsistent format that consultation notes are written in. Our knowledge graphs include information about existing patient symptoms, their duration, and their severity. We apply our framework to consultation notes of COVID-19 patients in the UK COVID-19 Clinical Assesment Servcie (CCAS) patient dataset. We provide a quantitative evaluation of the performance of our framework, demonstrating that our approach has better accuracy than traditional NLP methods when answering questions about patients

Inferring Operational Requirements from Scenarios and Goal Models Using Inductive Learning

Goal orientation is an increasingly recognised Requirements Engineering paradigm. However, integration of goal modelling with operational models remains an open area for which the few techniques that exist are cumbersome and impractical. In particular, the derivation of operational models and operational requirements from goals is a manual and tedious task which is, currently, only partially supported by operationalisation patterns. In this position paper we propose a framework for supporting such tasks by combining model checking and machine learning. As a proof of concept we instantiate the framework to show that progress checks and inductive learning can be used to infer preconditions and hence to support derivation of operational models