Author name cluster

Matthew Crosby

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

IJCAI Conference 2022 Conference Paper

Detect, Understand, Act: A Neuro-Symbolic Hierarchical Reinforcement Learning Framework (Extended Abstract)

Ludovico Mitchener
David Tuckey
Matthew Crosby
Alessandra Russo

We introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for effectively learning symbolic meta-policies over options using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges.

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JAIR Journal 2021 Journal Article

The Societal Implications of Deep Reinforcement Learning

Jess Whittlestone
Kai Arulkumaran
Matthew Crosby

Deep Reinforcement Learning (DRL) is an avenue of research in Artificial Intelligence (AI) that has received increasing attention within the research community in recent years, and is beginning to show potential for real-world application. DRL is one of the most promising routes towards developing more autonomous AI systems that interact with and take actions in complex real-world environments, and can more flexibly solve a range of problems for which we may not be able to precisely specify a correct ‘answer’. This could have substantial implications for people’s lives: for example by speeding up automation in various sectors, changing the nature and potential harms of online influence, or introducing new safety risks in physical infrastructure. In this paper, we review recent progress in DRL, discuss how this may introduce novel and pressing issues for society, ethics, and governance, and highlight important avenues for future research to better understand DRL’s societal implications. This article appears in the special track on AI and Society.

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ICAPS Conference 2017 Conference Paper

Integrating Mission and Task Planning in an Industrial Robotics Framework

Matthew Crosby
Ronald P. A. Petrick
Francesco Rovida
Volker Krüger

This paper presents a framework developed for an industrial robotics system that utilises two different planning components. At a high level, a multi-robot mission planner interfaces with a fleet and environment manager and uses multiagent planning techniques to build mission assignments to be distributed to a robot fleet. On each robot, a task planner automatically converts the robot's world model and skill definitions into a planning problem which is then solved to find a sequence of actions that the robot should perform to complete its mission. This framework is demonstrated on an industrial kitting task in a real-world factory environment.

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ECAI Conference 2014 Conference Paper

A Single-Agent Approach to Multiagent Planning

Matthew Crosby
Anders Jonsson 0001
Michael Rovatsos

In this paper we present a novel approach to multiagent planning in domains with concurrent actions and associated concurrent action constraints. In these domains, we associate the actions of individual agents with subsets of objects, which allows for a transformation of the problems into single-agent planning problems that are considerably easier to solve. The transformation forces agents to select joint actions associated with a single subset of objects at a time, and ensures that the concurrency constraints on this subset are satisfied. Joint actions are serialised such that each agent performs their part of the action separately. The number of actions in the resulting single-agent planning problem turns out to be manageable in many real-world domains, thus allowing the problem to be solved efficiently using a standard single-agent planner. We also describe a cost-optimal algorithm for compressing the resulting plan, i. e. merging individual actions in order to reduce the total number of joint actions. Results show that our approach can handle large problems that are impossible to solve for most multiagent planners.

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ICAPS Conference 2013 Conference Paper

Automated Agent Decomposition for Classical Planning

Matthew Crosby
Michael Rovatsos
Ronald P. A. Petrick

Many real-world planning domains, including those used in common benchmark problems, are based on multiagent scenarios. It has long been recognised that breaking down such problems into sub-problems for individual agents may help reduce overall planning complexity. This kind of approach is especially effective in domains where interaction between agents is limited. In this paper we present a fully centralised, offline, sequential, total-order planning algorithm for solving classical planning problems based on this idea. This algorithm consists of an automated decomposition process and a heuristic search method designed specifically for decomposed domains. The decomposition method is part of a preprocessing step and can be used to determine the 'multiagent nature' of a planning problem prior to actual plan search. The heuristic search strategy is shown to effectively exploit any decompositions that are found and performs significantly better than current approaches on loosely coupled domains.

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