T. L. Mccluskey

University of Huddersfield · Department of Informatics

Rolling Stock Preventative Maintenance (PM) is crucial for a safe and reliable railway system. Preventative Maintenance is periodically performed based on certification intervals. PM scheduling optimisation is required to maintain the desired level of safety and a reasonable maintenance cost and fleet availability. However, this process, as of toda...

The railway industry forecasts growth in passenger and freight traffic over the next 30 years. This places additional demands on rolling stock depot facilities, many of which were designed and built before the modern age of information technology. This paper explores the potential of improving the efficiency and effectiveness of rolling stock maint...

There is a growing interest in the use of AI techniques for urban traffic control, with a particular focus on traffic signal optimisation. Model-based approaches such as planning demonstrated to be capable of dealing in real-time with unexpected or unusual traffic conditions, as well as with the usual traffic patterns. Further, the knowledge models...

Rolling stock preventive maintenance (PM) is a key element in ensuring a safe and reliable railway system. PM, also known as periodic maintenance, is based on the age (distance or mileage) of the fleet where the intervals are specified carefully for each type of maintenance. PM scheduling optimisation is required to maintain the planned level of sa...

For a more robust robot capable of adapting to the changing environment, the goal of this work is to bridge the gap between abstract plans and robot action execution. Our platform combines planning, reasoning and learning new success values incrementally based on experience. Refinement involves reasoning over action execution failure using anomaly...

To support traffic authorities in the assessment of traffic signal strategies via simulation, we propose an approach that leverages on the strengths of automated planning knowledge models to generate accurate traffic simulators. By exploiting the sensors’ readings of adaptive traffic control systems in operation in a region of interest, and the con...

In this paper we describe work in progress investigating how to use ontological knowledge to facilitate goal-directed planning and scheduling, to help in a Virtual Depot project which features the automation of railway depot maintenance operations. Potential applications of AI planning within such integrated applications ranges from optimisation of...

In this paper we describe work in progress investigating how to use ontological knowledge to facilitate goal-directed planning and scheduling, to help in a Virtual Depot project which features the automation of railway depot maintenance operations. Potential applications of AI planning within such integrated applications ranges from optimisation of...

In the last decade the emphasis on improving the operational performance of domain independent automated planners has been in developing complex techniques which merge a range of different strategies. This quest for operational advantage, driven by the regular international planning competitions, has not made it easy to study, understand and predic...

The separation of planner logic from domain knowledge supports the use of reformulation and configuration techniques, such as macro-actions and entanglements, which transform the model representation in order to improve a planner’s performance. One drawback of such an approach is that it may require a potentially expensive training phase. In this p...

In this paper we consider the opportunities for KEPS within wide-spectrum projects which are aimed at creating precise ontological models of areas of industry. The goals of such projects are wide ranging, as is the related areas of enterprise modelling. Particularly in industrial applications, the benefits of ontological modelling are not only seem...

The development of domain-independent planners within the AI planning community is leading to “off-the-shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach—in which planners and domain knowledge are modules of larger software applications—that facilitates substitutions or improvements of individ...

Design patterns are widely used in various areas of computer science, the most notable example being software engineering. They have been introduced also for supporting the encoding of automated planning knowledge models, but up till now, with little success. In this paper, we investigate the merits of design patterns, as an example of the broader...

The requirement for autonomous robots to exhibit higher-level cognitive skills by planning and adapting in an ever changing environment and situation is indeed a great challenge for the AI community. In robotics task planning, the typical use of automated planners entails using fixed action descriptions that neglect the subtle differences that appe...

The development of domain-independent planners within the AI Planning community is leading to "off-the-shelf" technology that can be used in a wide range of applications. Moreover, it allows a modular approach --in which planners and domain knowledge are modules of larger software applications-- that facilitates substitutions or improvements of ind...

The creation and maintenance of a domain model is a well recognised bottleneck in the use of automated planning; indeed, ensuring a planning engine is fed with an accurate model of an application is essential in order that generated plans are effective. Engineering domain models using a hybrid representation is particularly challenging as it requir...

Abstract Precision and ultra-precision surfaces are crucial for many products – quality optics, joint & cranial implants, turbine blades, and industrial moulds & dies, to name a few. Automation in this context is distinct from standard procedures in industry, where the identical sequence of operations can be repeated over and over again. Ultrapreci...

In the last decade, planning with domains modelled in the hybrid PDDL+ formalism has been gaining significant research interest. A number of approaches have been proposed that can handle PDDL+, and their exploitation fostered the use of planning in complex scenarios. In this paper we introduce a PDDL+ reformulation method that reduces the size of t...

In the field of automated planning, the central research focus is on domain‐independent planning engines that accept planning tasks (domain models and problem descriptions) in a description language, such as Planning Domain Definition Language, and return solution plans. The performance of planning engines can be improved by gathering additional kn...

Improving a city’s infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment...

Domain independent planning engines accept a planning task description in a language such as PDDL and return a solution plan. Performance of planning engines can be improved by gathering additional knowledge about a class of planning tasks. In this paper we present Outer Entanglements, relations between planning operators and predicates, that are u...

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road network is a pivotal challenge, particularly in the case of unexpected situations. In order to tackle this challenge, approaches based on...

The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outsid...

Automated planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domain-independent planning, is the application knowledge that must be added to the planner to create a complete planning application. This is made explicit in (...

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. Optimising the exploitation of urban road network, while attempting to minimise the effects of traffic emissions, is a great challenge. SimplyfAI was a UK research council grant funded project whic...

Automated planning and scheduling are increasingly utilised in solving evsery day planning task. Planning in domains with continuous numeric changes present certain limitations and tremendous challenges to advanced planning algorithms. There are many pertinent examples to the engineering community; however, a case study is provided through the urba...

In the context of Industrie 4.0, we have previously described the roles of robots in optical processing, and their complementarity with classical CNC machines, providing both processing and automation functions. After having demonstrated robotic moving of parts between a CNC polisher and metrology station, and auto-fringe-acquisition, we have moved...

This paper is an experience report on the results of an industry-led collaborative project aimed at automating the control of traffic flow within a large city centre. A major focus of the automation was to deal with abnormal or unexpected events such as roadworks, road closures or excessive demand, resulting in periods of saturation of the network...

Report for Defence Science and Technology Laboratory (Dstl) Defence and Security Analysis Division, Ministry of Defence.

We review the 2016 International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS), the fifth in a series of competitions started in 2005. The ICKEPS series focuses on promoting the importance of knowledge engineering methods and tools for automated planning and scheduling systems.

One of the most persistent problems that plague modern-day road transport facilities is the quality of service provided. Especially during rush hours, this expensive infrastructure does not operate at capacity nor does it provide the level of service required by its users. Congestion has become a problem with severe economic and environmental reper...

Advanced urban traffic control (UTC) systems are often based on feedback algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible coordination in road (sub)networks based on measured traffic...

Creating a neural network based classification model is traditionally accomplished using the trial and error technique. However, the trial and error structuring method nornally suffers from several difficulties including overtraining. In this article, a new algorithm that simplifies structuring neural network classification models has been proposed...

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road networks is a pivotal challenge. Existing urban traffic control approaches, based on complex mathematical models, can effectively deal wi...

This paper describes the conceptual model underlying the Knowledge Engineering Web Interface (KEWI) which primarily aims to be used for modelling planning tasks in a semi-formal framework. This model consists of three layers: a rich ontology, a model of basic actions, and more complex methods. It is this structured conceptual model based on the ric...

This article reviews the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess the state of the art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part...

In this work we present SOMA: a Trend Mining framework, based on longitudinal data analysis, that is able to measure the interestingness of the produced trends in large noisy medical databases. Medical longitudinal data typically plots the progress of some medical condition, thus implicitly contains a large number of trends. The approach has been e...

Automated planning, which deals with the problem of generating sequences of actions, is an emerging research topic due to its potentially wide range of real-world application domains. As well as developing and improving planning engines, the acquisition of domain-specific knowledge is a promising way to improve the planning process. Domain-specific...

Today’s societies are facing great challenges in transforming living environments in a way better serving people’s demands of the future. A key point in this transformation is reinventing cities as smart cities, where the core services are integrated in a way that ensures a high quality of life while minimizing the usage of resources [Smart cities...

Research into techniques that reformulate problems to make general solvers more efficiently derive solutions has attracted much attention, in particular when the reformulation process is to some degree solver and domain independent. There are major challenges to overcome when applying such techniques to automated planning, however: reformulation me...

Ontology design is a crucial task for the Semantic Web. In the literature, methodologies have been proposed to develop ontologies, however the phase between knowledge gathering and knowledge coding remains challenging. In this paper, we propose a dynamic ontology design based on dynamic design notations for a systematic identification of the relati...

This paper introduces the Knowledge EngineeringWeb Interface (KEWI) which primarily aims to be used for modelling automated planning tasks in a semi-formal framework. The conceptual model used to represent the declarative and procedural knowledge in KEWI is described formally. The model consists of three layers: a rich ontology, a model of basic ac...

Computing statistical dependence of terms in textual documents is a widely studied subject and a core problem in many areas of science. This study focuses on such a problem and explores the techniques of estimation using the expected mutual information measure. A general framework is established for tackling a variety of estimations: (i) general fo...

Advanced urban traffic control systems are often based on feed-back algorithms. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations...

Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when...

Internet has become an essential component of our everyday social and financial activities. Nevertheless, internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customer’s confidence in e-commerce and online banking. Phis...

The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for artificial intelligence (AI) planning. This paper describes Learning Object-Centred Models (LOCM), a system that carries out the automated generation of a planning domain model from example training plans. The novelty of LOCM is tha...

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card cred...

Phishing is described as the art of emulating a website of a creditable firm intending to grab user’s private information such as usernames, passwords and social security number. Phishing websites comprise a variety of cues within its content-parts as well as browser-based security indicators. Several solutions have been proposed to tackle phishing...

Analysing the structures of solution plans generated by AI Planning engines is helpful in improving the generative planning process, as well as shedding light in the study of its theoretical foundations.We investigate a specific property of solution plans, that we called linearity, which refers to a situation where each action achieves an atom (or...

In Automated Planning, learning and exploiting additional knowledge within a domain model, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising because they are to some extent domain mod...

Encoding a planning domain model is a complex task in realistic applications. It includes the analysis of planning application requirements, formulating a model that describes the domain, and testing it with suitable planning engines. In this paper we introduce a variety of new planning domains, and we then use and evaluate three separate strategie...

Automated planning even in its simplest form, classical planning, is a computationally hard problem. With the increasing involvement of intelligent systems in everyday life there is a need for more and more advanced planning techniques able to solve planning problems in little (or real) time. However, planners designed to solve planning problems as...

The problem of automated planning is known to be intractable in general. Moreover, it has been proven that in some cases finding an optimal solution is much harder than finding any solution. Existing techniques have to compromise between speed of the planning process and quality of solutions. For example, techniques based on greedy search often are...

In this paper we describe a project (IMPRESS) in which machine learning (ML) tools were created and utilised for the validation of an Air Traffic Control domain theory written in first order logic. During the project, novel techniques were devised for the automated revision of general clause form theories using training examples. These techniques w...

Corporations that offer online trading can achieve a competitive edge by serving worldwide clients. Nevertheless, online trading faces many obstacles such as the unsecured money orders. Phishing is considered a form of internet crime that is defined as the art of mimicking a website of an honest enterprise aiming to acquire confidential information...

We are recently experiencing an unprecedented explosion of available data coming from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raise...

An important area in AI Planning is the expressiveness of planning domain specification languages such as PDDL, and their aptitude for modelling real applications. This paper presents OCLplus, an extension of a hierarchical object centred planning domain definition language, intended to support the representation of domains with continuous change....

Automated planning is a well studied research topic thanks to its wide range of real-world applications. Despite significant progress in this area many planning problems still remain hard and challenging. Some techniques such as learning macro-operators improve the planning process by reformulating the (original) planning problem. While many encour...

Much progress has been made in the research and development of automated planning algorithms in recent years. Though incremental improvements in algorithm design are still desirable, complementary approaches such as problem reformulation are important in tackling the high computational complexity of planning. While machine learning and adaptive tec...

This paper introduces a novel path planning technique called MCRT which is aimed at non-deterministic, partially known, real-time domains populated with dynamically moving obstacles, such as might be found in a real-time strategy (RTS) game. The technique combines an efficient form of Monte-Carlo tree search with the randomized exploration capabili...

In this paper, we present an innovative approach coupling active contours with an ontological representation of knowledge, in order to understand scenes acquired by a moving camera and containing multiple non-rigid objects evolving over space and time. The developed active contours enable both segmentation and tracking of multiple targets in each c...

Planning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI) with broad practical applicability. Many real-world problems can be formulated as AI planning and scheduling (P&S) problems, where resources must be allocated to optimize overall performance objectives. Frequently, solving these problems requires an...

Associative classification integrates association rule and classification in data mining to build classifiers that are highly accurate than that of traditional classification approaches such as greedy and decision tree. However, the size of the classifiers produced by associative classification algorithms is usually large and contains insignificant...

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece, in September 2009. We give an overview of how the competition has developed since its first run in 2005 and its relationship with the AI planning field. This run of the competition focused...

Associative classification is a branch in data mining that employs association rule discovery methods in classification problems. In this paper, we introduce a novel data mining method called Looking at the Class (LC), which can be utilised in associative classification approach. Unlike known algorithms in associative classification such as Classif...

AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread u...

The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a nam...

A major area of recent Web-related research concerns automated web service composition. A major advantage of Web services technology lies in the potential of creating value-added services by combining existing ones to achieve customized tasks. How to combine these services efficiently into an arrangement that is both functionally sound and architec...

The problem of formulating knowledge bases containing ac- tion schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a n...

AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread u...

Associative classification (AC) is a branch in data mining that utilises association rule discovery methods in classification problems. In this paper, we propose a new training method called Looking at the Class (LC), which can be adapted by any rule-based AC algorithm. Unlike the traditional Classification based on Association rule (CBA) training...

In this paper an object-centric perspective on planning domain definition is presented along with an overview of GIPO (graphical interface for planning with objects), a supporting tools environment. It is argued that the object-centric view assists the ...

In this paper an object-centric perspective on planning domain definition is presented along with an overview of GIPO (graphical interface for planning with objects), a supporting tools environment. It is argued that the object-centric view assists the domain developer in conceptualizing the domain’s structure, and we show how GIPO enables the deve...

Domain modelling for AI Planning can be a complex process especially if there is a large number of objects or actions or both to be modelled. This task can be facilitated by tools which induce operators or methods from examples. Further, large and complex domains are more easily constructed if do-main languages are used which allow for hierarchical...

In this paper we describe work in progress that aims to de- velop a domain-independent tool set which supports the cre- ation and analysis of domain descriptions and plans contain- ing continuously changing processes, instantaneous events, and actions. The tools described are (i) a life history editor that enables a designer to create a domain desc...