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Peter Gregory

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10 papers

2 author rows

ICAPS Conference 2017 Conference Paper

Framer: Planning Models from Natural Language Action Descriptions

Alan Lindsay
Jonathon Read
João F. Ferreira 0001
Thomas Hayton
Julie Porteous
Peter Gregory

In this paper, we describe an approach for learning planning domain models directly from natural language (NL) descriptions of activity sequences. The modelling problem has been identified as a bottleneck for the widespread exploitation of various technologies in Artificial Intelligence, including automated planners. There have been great advances in modelling assisting and model generation tools, including a wide range of domain model acquisition tools. However, for modelling tools, there is the underlying assumption that the user can formulate the problem using some formal language. And even in the case of the domain model acquisition tools, there is still a requirement to specify input plans in an easily machine readable format. Providing this type of input is impractical for many potential users. This motivates us to generate planning domain models directly from NL descriptions, as this would provide an important step in extending the widespread adoption of planning techniques. We start from NL descriptions of actions and use NL analysis to construct structured representations, from which we construct formal representations of the action sequences. The generated action sequences provide the necessary structured input for inducing a PDDL domain, using domain model acquisition technology. In order to capture a concise planning model, we use an estimate of functional similarity, so sentences that describe similar behaviours are represented by the same planning operator. We validate our approach with a user study, where participants are tasked with describing the activities occurring in several videos. Then our system is used to learn planning domain models using the participants' NL input. We demonstrate that our approach is effective at learning models on these tasks.

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ICAPS Conference 2016 Conference Paper

Domain Model Acquisition in Domains with Action Costs

Peter Gregory
Alan Lindsay

This paper addresses the challenge of automated numeric domain model acquisition from observations. Many industrial and commercial applications of planning technology rely on numeric planning models. For example, in the area of autonomous systems and robotics, an autonomous robot often has to reason about its position in space, power levels and storage capacities. It is essential for these models to be easy to construct. Ideally, they should be automatically constructed. Learning the structure of planning domains from observations of action traces has produced successful results in classical planning. In this work, we present the first results in generalising approaches from classical planning to numeric planning. We restrict the numeric domains to those that include fixed action costs. Taking the finite state automata generated by the LOCM family of algorithms, we learn costs associated with machines; specifically to the object transitions and the state parameters. We learn action costs from action traces (with only the final cost of the plans as extra information) using a constraint programming approach. We demonstrate the effectiveness of this approach on standard benchmarks.

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IJCAI Conference 2016 Conference Paper

Domain Model Acquisition in the Presence of Static Relations in the LOP System

Peter Gregory
Stephen Cresswell

We present a new domain model acquisition algorithm, LOP, that induces static predicates by using a combination of the generalised output from LOCM2 and a set of optimal plans as input to the learning system. We observe that static predicates can be seen as restrictions on the valid groundings of actions. Without the static predicates restricting possible groundings, the domains induced by LOCM2 produce plans that are typically shorter than the true optimal solutions. LOP works by finding a set of minimal static predicates for each operator that preserves the length of the optimal plan.

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ICAPS Conference 2015 Conference Paper

Domain Model Acquisition in the Presence of Static Relations in the LOP System

Peter Gregory
Stephen Cresswell

This paper addresses the problem of domain model acquisition from only action traces when the underlying domain model contains static relations. Domain model acquisition is the problem of synthesising a planning domain model from example plan traces and potentially other information, such as intermediate states. The LOCM and LOCMII domain model acquisition systems are highly effective at determining the dynamics of domain models with only plan traces as input (i. e. they do not rely on extra inputs such as predicate definitions, initial, final and intermediate states or invariants). Much of the power of the LOCM family of algorithms comes from the assumption that each action parameter goes through a transition. One place that this assumption is too strong is in the case of static predicates. We present a new domain model acquisition algorithm, LOP, that induces static predicates by using a combination of the generalised output from LOCM2 and a set of optimal plans as input to the learning system. We observe that static predicates can be seen as restrictions on the valid groundings of actions. Without the static predicates restricting possible groundings, the domains induced by LOCMII produce plans that are typically shorter than the true optimal solutions. LOP works by finding a minimal static predicate for each operator that preserves the length of the optimal plan.

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ICAPS Conference 2014 Conference Paper

Automated Planning for Multi-Objective Machine Tool Calibration: Optimising Makespan and Measurement Uncertainty

Simon Parkinson
Peter Gregory
Andrew Longstaff
Andrew Crampton

The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine’s capabilities are assessed. Machine tool downtime is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer’s time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. The work presented in this paper is focussed on expanding a developed temporal optimisation model to reduce the uncertainty of measurement. Encoding the knowledge in regular PDDL requires the discretization of non-linear, continuous temperature change and implementing the square root function. The implementation shows that not only can domainindependent automated planning reduce machine downtime by 10. 6% and the uncertainty of measurement by 59%, it is also possible to optimise both metrics reaching a compromise that is on average 9% worse that the best-known solution for each individual metric.

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ICAPS Conference 2012 Conference Paper

Planning Modulo Theories: Extending the Planning Paradigm

Peter Gregory
Derek Long
Maria Fox 0001
J. Christopher Beck

Considerable effort has been spent extending the scope of planning beyond propositional domains to include, for example, time and numbers. Each extension has been designed as a separate specific semantic enrichment of the underlying planning model, with its own syntax and customised integration into a planning algorithm. Inspired by work on SAT Modulo Theories (SMT) in the SAT community, we develop a modelling language and planner that treat arbitrary first order theories as parameters. We call the approach Planning Modulo Theories (PMT). We introduce a modular language to represent PMT problems and demonstrate its benefits over PDDL in expressivity and compactness. We present a generalisation of the $h_{max}$ heuristic that allows our planner, PMTPlan, to automatically reason about arbitrary theories added as modules. Over several new and existing benchmarks, exploiting different theories, we show that PMTPlan can significantly out-perform an existing planner using PDDL models.

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ICAPS Conference 2012 Conference Paper

The Application of Automated Planning to Machine Tool Calibration

Simon Parkinson
Andrew Longstaff
Andrew Crampton
Peter Gregory

Engineering companies working with machine tools will often be required to calibrate those machines to international standards. The calibration process requires various errors in the machine to be measured by a skilled expert. In addition to conducting the tests, the engineer must also plan the order in which the tests should take place, and also which instruments should be used to perform each test. It is critical to find as short a calibration plan as possible so that the machine is not out of service for too long. In this work, automated planning is applied to the problem of generating machine tool calibration plans. Given a description of a machine, and its various axes, we produce a calibration plan that minimises the time taken to measure all of the errors of a machine. We also consider the case when there is not enough time to test all errors of the machine, and the calibration plan must maximise the importance of the set of errors tested in the limited time available.

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AAAI Conference 2011 Conference Paper

Exploiting Path Refinement Abstraction in Domain Transition Graphs

Peter Gregory
Derek Long
Craig McNulty
Susan Murphy

Partial Reﬁnement A-Star (PRA ) is an abstraction technique, based on clustering nearby nodes in graphs, useful in large path-planning problems. Abstracting the underlying graph yields a simpler problem whose solution can be used, by reﬁnement, as a guide to a solution to the original problem. A fruitful way to view domain independent planning problems is as a collection of multi-valued variables that must perform synchronised transitions through graphs of possible values, where the edges are deﬁned by the domain actions. Planning involves ﬁnding efﬁcient paths through Domain Transition Graphs (DTGs). In problems where these graphs are large, planning can be prohibitively expensive. In this paper we explore two ways to exploit PRA in DTGs.

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ICAPS Conference 2011 Conference Paper

Generalised Domain Model Acquisition from Action Traces

Stephen Cresswell
Peter Gregory

One approach to the problem of formulating domain models for planning is to learn the models from example action sequences. The LOCM system demonstrated the feasibility of learning domain models from example action sequences only, with no observation of states before, during or after the plans. LOCM uses an object-centred representation, in which each object is represented by a single parameterised state machine. This makes it powerful for learning domains which fit within that representation, but there are some well-known domains which do not. This paper introduces LOCM2, a novel algorithm in which the domain representation of LOCM is generalised to allow multiple parameterised state machines to represent a single object. This extends the coverage of domains for which an adequate domain model can be learned. The LOCM2 algorithm is described and evaluated by testing domain learning from example plans from published results of past International Planning Competitions.

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ECAI Conference 2010 Conference Paper

Constraint Based Planning with Composable Substate Graphs

Peter Gregory
Derek Long
Maria Fox 0001

Constraint satisfaction techniques provide powerful inference algorithms that can prune choices during search. Constraint-based approaches provide a useful complement to heuristic search optimal planners. We develop a constraint-based model for cost-optimal planning that uses global constraints to improve the inference in planning.

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