Automated planning requires explicit domain knowledge, typically represented in PDDL, to generate effective solutions. The process of formulating, maintaining, and validating this knowledge is the cornerstone of Knowledge Engineering for Planning and Scheduling (KEPS). Although Large Language Models (LLMs) have shown promise for automated planning tasks, and are gaining popularity in the field, their impact on KEPS remains unexplored. In this paper we investigate the potential of LLMs to streamline and enhance the KEPS field, by taking a close look at the processes used to develop explicit symbolic knowledge models in safety-related applications. The paper's findings are that while LLMs can assist in knowledge acquisition and formulation, human domain expertise and external symbolic validators remain indispensable for ensuring correctness, operationality and completeness of planning applications.

The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal strategies remain, as existing approaches tend not to consider constraints and features of the actual real-world infrastructure on which they will be implemented. To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.

The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal plans remain, as planning systems need to consider constraints and features of the actual real-world infrastructure on which they will be implemented. To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.

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 on which such techniques rely to generate traffic signal strategies are in fact simulation models of traffic, hence can be used by traffic authorities to test and compare different approaches. In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. To demonstrate the capabilities of the framework, we consider real-world data collected from sensors deployed in a major corridor of the Kirklees region of the United Kingdom.

Automated planning is a prominent AI challenge, and it is now exploited in a range of real-world applications. There are three crucial aspects of automated planning: the planning engine, the domain model, and the problem instance. While the planning engine and the domain model can be engineered and optimised offline, in many applications there is the need to generate problem instances on the fly. In this paper we focus on the challenges of on-the-fly knowledge acquisition for complex and variegated problem instances. We consider as a case study the application of planning to urban traffic control and we describe the designed and developed knowledge acquisition process. This allows us to discuss a range of lessons learned from the experience, and to point to important lines of research to support the knowledge acquisition process for automated planning applications.

Automated planning is a prominent Artificial Intelligence challenge, as well as a requirement for intelligent autonomous agents. A crucial aspect of automated planning is the knowledge model, that includes the relevant aspects of the application domain and of a problem instance to be solved. Despite the fact that the quality of the model has a strong influence on the resulting planning application, the notion of quality for automated planning knowledge models is not well understood, and the engineering process in building such models is still mainly an ad-hoc process. In order to develop systematic processes that support a more comprehensive notion of quality, this paper, building on existing frameworks proposed for general conceptual models, introduces a quality framework specifically focused on automated planning knowledge models.

Abstract 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 knowledge about specific planning domain models/tasks (such as control rules) that can narrow the search for a solution plan. Such knowledge is often learned from training plans and solutions of simple tasks. Using techniques to reformulate the given planning task to incorporate additional knowledge, while keeping to the same input language, allows to exploit off‐the‐shelf planning engines. In this paper, we present inner entanglements that are relations between pairs of operators and predicates that represent the exclusivity of predicate achievement or requirement between the given operators. Inner entanglements can be encoded into a planner's input language by transforming the original planning task; hence, planning engines can exploit them. The contribution of this paper is to provide an in‐depth analysis and evaluation of inner entanglements, covering theoretical aspects such as complexity results, and an extensive empirical study using International Planning Competition benchmarks and state‐of‐the‐art planning engines.

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 used to restrict the number of operator instances. Outer Entanglements can be encoded within a planning task description, effectively reformulating it. We provide an in depth analysis and evaluation of outer entanglements illustrating the effectiveness of using them as generic heuristics for improving the efficiency of planning engines.

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 which was aimed towards solving air quality problems caused by road traffic emissions. Large cities such as Manchester struggle to meet air quality limits as the range of available traffic management devices is limited. In the study, we investigated the application of linked data to enrich environmental and traffic data feeds, and we used this with automated planning tools to enable traffic to be managed at a region level. The management will have the aim of avoiding air pollution problems before they occur. This demo focuses on the planning component, and in particular the engineering and validation aspects, that were pivotal for the success of the project.

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 (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by automated planning engines for reasoning, in order to synthesize a solution plan. Formulating knowledge for use in planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers.

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 paper, we introduce heuristic approaches for the online configuration of planning domain models. The proposed heuristics consider different aspects of PDDL-encoded operators for reordering such operators in the domain model, relying on the assumption that the way in which operators are encoded carries useful information about their expected use.

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 with planned-ahead events, but are not able to cope with unexpected situations --such as roads blocked due to car accidents or weather-related events-- because of their huge computational requirements. Therefore, such unexpected situations are mainly dealt with manually, or by exploiting pre-computed policies. Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. We present a PDDL+ formulation of urban traffic control, where continuous processes are used to model flows of cars, and show how planning can be used to efficiently reduce congestion of specified roads by controlling traffic light green phases. We present simulation results on two networks (one of them considers Manchester city centre) that demonstrate the effectiveness of the approach, compared with fixed-time and reactive techniques.

Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing website classification problem and it shows competitive results with respect to various evaluation measures such as harmonic mean (F1-score), precision, and classification accuracy.

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 predict what combinations of techniques will have what effect on a planner’s behaviour in a particular application domain. In this paper, we consider two machine learning techniques for planner performance improvement, and exploit a modular approach to their combination in order to facilitate the analysis of the impact of each individual component. We believe this can contribute to the development of more transparent planning engines, which are designed using modular, interchangeable, and well-founded components. Specifically, we combined two previously unrelated learning techniques, entanglements and relational decision trees, to guide a “vanilla” search algorithm. We report on a large experimental analysis which demonstrates the effectiveness of the approach in terms of performance improvements, resulting in a very competitive planning configuration despite the use of a more modular and transparent architecture. This gives insights on the strengths and weaknesses of the considered approaches, that will help their future exploitation.

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 relations between domain concepts. For this purpose, we propose the use of the Unified Modeling Language (UML) and the Business Process Modeling Notation (BPMN), and the mapping of the related dynamic notations to the ontology domain. Our approach has been successfully validated in a study case of an ontology with a publication repository domain.

Abstract: This paper introduces the Knowledge Engineering Web 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 actions, and more complex methods. It is this structured conceptual model based on the rich ontology that facilitates knowledge engineering. The focus of this paper is to show how the central knowledge model used in KEWI differs from a model directly encoded in PDDL, the language accepted by most existing planning engines. Specifically, the rich ontology enables a more concise and natural style of representation. For operational use, KEWI automatically generates PDDL. Initial experiments show that the generated PDDL can be processed by a planner without incurring significant drawbacks. 1

Phishing is described as the art of echoing 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 the browser‐based security indicators provided along with the website. Several solutions have been proposed to tackle phishing. Nevertheless, there is no single magic bullet that can solve this threat radically. One of the promising techniques that can be employed in predicting phishing attacks is based on data mining, particularly the ‘induction of classification rules’ since anti‐phishing solutions aim to predict the website class accurately and that exactly matches the data mining classification technique goals. In this study, the authors shed light on the important features that distinguish phishing websites from legitimate ones and assess how good rule‐based data mining classification techniques are in predicting phishing websites and which classification technique is proven to be more reliable.

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 fast as possible often provide solution plans of low quality. The quality of solution plans can be improved by their post-planning analysis by which redundant actions or optimizable sub plans can be identified. In this paper, we present techniques for determining redundancy of actions in plans. Especially, we present techniques for efficient redundancy checking of pairs of inverse actions. These techniques are accompanied with necessary theoretical foundations and are also empirically evaluated using existing planning systems and standard planning benchmarks.

In this paper we describe a project (IMPRESS) which utilised a machine learning tool for the validation of an air traffic control domain theory. During the project, novel techniques were devised for the automated revision of general clause form theories using training examples. This technique involves focusing in on the parts of a theory which involve ordinal sorts, and applying geometrical revision operators to repair faulty component parts. The method is illustrated with experimental results obtained during the project.

Our research concerns formal expressive, object centred languages and tools for use in engineering domains for planning applications. We extend our recent work on an object centred language for encoding precondition planning domains to a language called OCL/sub h/, designed for HTN planning. Domain encodings for HTN planners are particularly troublesome, because they tend to be used in knowledge based applications requiring a great deal of 'domain engineering', and the abstract operators central to an HTN model do not share the fairly clear declarative semantics of concrete pre- and post-condition operators. Central to our approach is the development, in parallel, of the abstract operator set and the hierarchical state specification of the objects that the operators manipulate. We also define and illustrate a transparency property, together with a transparency checking tool, which helps the developer to encode a clear planning model in OCL/sub h/. Our encoding of the Translog domain is used as an extended example to illustrate the approach.

We describe a tools environment which automates the validation and maintenance of a requirements model written in many-sorted first order logic. We focus on a translator that produces an executable form of the model; blame assignment functions, which input batches of mis-classified tests (i.e. training examples) and output likely faulty parts of the model; and a theory reviser; which inputs the faulty parts and examples and outputs suggested revisions to the model. In particular we concentrate on the problems encountered when applying these tools to a real application: a requirements model containing air traffic control separation standards, operating methods and airspace information.

A great deal of emphasis in classical AI planning research has been placed on search-control issues in plan generation, while the issue of knowledge representation and acquisition of models for use with classical planning engines has been largely ignored. Work in knowledge-based planning, on the other hand, is often associated with 'scruffy' AI, there being no standard representation languages with associated formal semantics for encoding domain models. In this paper we describe a method to create a planning domain model which preserves the domain independence, generality and 'clean' properties of generative planners to which the model can be attached. Our method is based an lifting the level of domain representation from the literal-centred, to the object-centred. This object-centred method has the advantage that it naturally allows for the creation of a supporting tools environment to help in (i) the creation and validation of a precise planning model, and (ii) the speed-up of plan generation.

Abstract This paper describes our experience in capturing, using a formal specification language, a model of the knowledge‐intensive domain of oceanic air traffic control. This model is intended to form part of the requirements specification for a decision support system for air traffic controllers. We give an overview of the methods we used in analysing the scope of the domain, choosing an appropriate formalism, developing a domain model, and validating the model in various ways. Central to the method was the development of a formal requirements engineering environment which provided automated tools for model validation and maintenance.