Mauro Vallati

University of Huddersfield · Department of Informatics

Answer set programming (ASP) has demonstrated its potential as an effective tool for concisely representing and reasoning about real-world problems. In this paper, we present an application in which ASP has been successfully used in the context of dynamic traffic distribution for urban networks, within a more general framework devised for solving s...

Answer Set Programming (ASP) has demonstrated its potential as an effective tool for concisely representing and reasoning about real-world problems. In this paper, we present an application in which ASP has been successfully used in the context of dynamic traffic distribution for urban networks, within a more general framework devised for solving s...

Background Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medi...

The PDDL+ formalism allows the use of planning techniques in applications that require the ability to perform hybrid discrete-continuous reasoning. PDDL+ problems are notoriously challenging to tackle, and to reason upon them a well-established approach is discretisation. Existing systems rely on a single discretisation delta or, at most, two: a si...

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 infras...

The PDDL+ formalism allows the use of planning techniques in applications that require the ability to perform hybrid discrete-continuous reasoning. PDDL+ problems are notoriously challenging to tackle, and to reason upon them a well-established approach is discretisation. Existing systems rely on a single discretisation delta or, at most, two: a si...

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-...

PDDL+ is an expressive formalism that allows for the use of planning in complex real-world applications. It includes a number of features designed to improve the readability and conciseness of the resulting knowledge models, but that are commonly doubted to have detrimental impact on the performance of domain-independent searches and heuristics. In...

Simple Summary The field of artificial intelligence (AI) is quickly becoming recognized for its potential to significantly improve medicine. AI is still in its infancy when it comes to treating Chronic Myeloid Leukemia (CML), which was once thought to be an easily treated cancer until TKIs were introduced and significantly increased patient surviva...

The advent of Connected Autonomous Vehicles (CAVs) paves the way to a new era of urban traffic control and management, driven by Artificial Intelligence (AI)-enabled strategies. This advancement promises significant improvements in infrastructure use optimization, traffic delay reduction, and overall sustainability. The autonomous driving capabilit...

The advent of Connected Autonomous Vehicles (CAVs) paves the way to a new era of urban traffic control and management, driven by Artificial Intelligence (AI)-enabled strategies. This advancement promises significant improvements in infrastructure use optimization, traffic delay reduction, and overall sustainability. The autonomous driving capabilit...

Background: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of med...

Vehicle-to-Everything (V2X) technology relies on wireless communication and coordination, aiming to improve road safety and traffic efficiency by orchestrating the interaction among the vehicles, infrastructures, and various entities. However, existing organizational and operational forms of V2X infrastructures encounter significant challenges, suc...

The current worldwide increasing trend in urbanisation is aggravating urban traffic congestion’s social, economic, and health burdens. The introduction of new means of transport, such as Connected Autonomous Vehicles, and the rise of Artificial Intelligence, is enabling a paradigm shift in urban traffic management and control from existing reactive...

This paper presents a novel framework for customized modular bus systems that leverages travel demand prediction and modular autonomous vehicles to optimize services proactively. The proposed framework addresses two prediction scenarios with different forward-looking operations: optimistic operation and pessimistic operation. A mixed integer progra...

Automated planning is a prominent area of Artificial Intelligence and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reaso...

pddl+ is an expressive planning formalism that enables the modelling of hybrid domains with both discrete and continuous dynamics. However, its expressiveness makes this language notoriously difficult to handle natively. To address this challenge, translations from time-discrete pddl+ into numeric pddl2.1 have been proposed as a way to reframe the...

Connected and Autonomous Vehicle (CAV) technologies have the potential to revolutionise public transport systems, making them more financially and environmentally sustainable, accessible, and user-centric. However, CAV-based bus services are vulnerable to cyber attacks manifesting in unwanted, deceitful behaviour. This includes behaviours such as s...

The integration of autonomous vehicles and on-demand customized bus systems is expected to be beneficial for responding to real-time demands. This paper investigates the autonomous customized bus (ACB) system that leverages passenger demand prediction to enhance service quality and vehicle utilization. A novel ACB service design optimization model...

Fifth International Competition on Computational Models of Argumentation (ICCMA'23)

Solver competitions play a prominent role in assessing and advancing the state of the art for solving many problems in AI and beyond. Notably, in many areas of AI, competitions have had substantial impact in guiding research and applications for many years, and for a solver to be ranked highly in a competition carries considerable weight. But to wh...

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...

PDDL+ is an expressive formalism that allows for the use of planning in hybrid discrete-continuous domains. To cope with unexpected situations, it is crucial for deployed planning-based systems to efficiently repair existing plans. In this paper, we revisit a recently proposed FIXABILITY framework for expressing and solving problems from validation...

The plan, execution, and replan framework has proven to be extremely valuable in complex real-world applications, where the dynamics of the environment cannot be fully encoded in the domain model. However, this comes at the cost of regenerating plans from scratch, which can be expensive when expressive formalisms like PDDL+ are used. Given the comp...

The emerging customized bus system based on modular autonomous electric vehicles (MAEVs) shows tremendous potential to improve the mobility, accessibility and environmental friendliness of a public transport system. However, the existing studies in this area almost focus on human-driven vehicles which face some striking limitations (e.g., restricte...

In the last decade, Process Mining has become a significant field to help healthcare process experts understand and gain relevant insights about the processes they execute. One of the most challenging questions in Process Mining, and particularly in healthcare, typically is: how good are the discovered models? Previous studies have suggested approa...

Background Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients’ quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM)...

Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason...

PDDL+ models are advanced models of hybrid systems and the resulting problems are notoriously difficult for planning engines to cope with. An additional limiting factor for the exploitation of PDDL+ approaches in real-world applications is the restricted number of domain-independent planning engines that can reason upon those models. With the aim o...

Process mining for healthcare is the discipline that focuses on mining, analysing, and enhancing real-world healthcare processes. In this chapter, we provide a compelling overview of the research field, and we take the occasion to highlight current challenges and promising research directions.

Planning with global state constraints is an extension of classical planning such that some properties of each state are derived via a set of rules common to all states. This approach is important for the application of planning techniques in manipulating cyber-physical systems, and has been shown to be effective in practice. Urban Traffic Control...

PDDL+ is an expressive planning formalism that enables the modelling of hybrid discrete-continuous domains. The resulting models are notoriously difficult to cope with, and few planning engines are natively supporting PDDL+. To foster the use of PDDL+, this paper revisits a set of recently proposed translations allowing to reformulate a PDDL+ task...

Motivated by the requirements of highly effective customized bus (CB) service and by the rapid growth of autonomous electric vehicles (AEVs), this paper studies a new optimization model for the autonomous electric customized bus (AECB) service, aiming at minimizing operating costs and improving vehicles’ efficient use. The proposed model contains t...

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...

PDDL+ allows the formal specification of systems representing mixed discrete-continuous representation, under both discrete and continuous dynamics; this expressiveness is pivotal in real-world applications. An important aspect is the capability of validating plans obtained by planning systems, and assessing their compliance against the domain's mo...

Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a...

Leveraging automated planning has been shown to be advantageous for automating network penetration testing, providing a foundation to generate intelligent approaches to attacking a target system. Unfortunately, this same technology has the potential to be abused by actual attackers, presenting a challenge to defenders. In this paper, we investigate...

The Connected and Autonomous Vehicle (CAV) is an emerging mobility technology that may hold a paradigm-changing potential for the future of transport policy and planning. Despite a wealth of likely benefits that have made their eventual launch inescapable, CAVs may also be a source of unprecedented disruption for tomorrow's travel eco-systems becau...

The decoupling between the representation of a certain problem, that is, its knowledge model, and the reasoning side is one of main strong points of model-based artificial intelligence (AI). This allows, for example, to focus on improving the reasoning side by having advantages on the whole solving process. Further, it is also well known that many...

The decoupling between the representation of a certain problem, i.e., its knowledge model, and the reasoning side is one of main strong points of model-based Artificial Intelligence (AI). This allows, e.g. to focus on improving the reasoning side by having advantages on the whole solving process. Further, it is also well-known that many solvers are...

pddl+ is an expressive planning formalism that enables the modelling of domains having both discrete and continuous dynamics. Recently, two mappings for translating discretised pddl+ problems into a numeric a-temporal task have been proposed. Such translations produce a task of exponential or polynomial size w.r.t. the size of the native task. In t...

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose mechanisms are still fully unclear. Being able to predict ALS prognosis would help in improving the patients’ quality of life and support clinicians in planning treatments. On the one hand, most of the modeling approaches to ALS miss to catch the evolving nature of the disease...

Automated planning is a prominent Artificial Intelligence (AI) challenge that has been extensively studied for decades, which has led to the development of powerful domain-independent planning systems. The performance of domain-independent planning systems are strongly affected by the structure of the search space, that is dependent on the applicat...

We present Fudge, an abstract argumentation solver that tightly integrates satisfiability solving technology to solve a series of abstract argumentation problems. While most of the encodings used by Fudge derive from standard translation approaches, Fudge makes use of completely novel encodings to solve the skeptical reasoning problem wrt. preferre...

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...

Restricting the search space has shown to be an effective approach for improving the performance of automated planning systems. A planner-independent technique for pruning the search space is domain and problem reformulation. Recently, Outer Entanglements, which are relations between planning operators and initial or goal predicates, have been intr...

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...

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...

In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. In this paper, we describe an approach for dealing with the in-station dispatching problem by means of automated planning techniques.

Automated planning is the field of Artificial Intelligence (AI) that focuses on identifying sequences of actions allowing to reach a goal state from a given initial state. The need of using such techniques in real-world applications has brought popular languages for expressing automated planning problems to provide direct support for continuous and...

In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. This is a fundamental problem for the whole railway network efficiency, and in turn for the transportation of goods and passengers, given that stations are among the most critical points in ne...

In this paper we introduce \(\textsf {AASExts}\), an algorithm for computing semi–stable extensions. We improve techniques developed for other semantics, notably preferred semantics, as well as leverage recent advances in All-SAT community. We prove our proposed algorithm is sound and complete, we describe the experiments to select the most appropr...

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...

We address the problem of deciding skeptical acceptance wrt. preferred semantics of an argument in abstract argumentation frameworks, i. e., the problem of deciding whether an argument is contained in all maximally admissible sets, a.k.a. preferred extensions. State-of-the-art algorithms solve this problem with iterative calls to an external SAT- s...

In railway networks, stations are probably the most critical points for interconnecting trains' routes: in a restricted geographical area, a potentially large number of trains have to stop according to an official timetable, with the concrete risk of accumulating delays that can then have a knockout effect on the rest of the network. In this contex...

Hybrid PDDL+ models are amongst the most advanced models of systems and the resulting problems are notoriously difficult for planning engines to cope with. An additional limiting factor for the exploitation of PDDL+ approaches in real-world applications is the restricted number of domain-independent planning engines that can reason upon those model...

Given the dynamic environment and the ever-changing international context, it is pivotal for companies to be able to quickly and effectively identify potential threats and opportunities. This can be done via environmental scanning, that allows to develop potential scenarios which help in proactively plan responses to potential risks. Yet, the proce...

The 13th Symposium on Combinatorial Search (SoCS) was held May 26 to 28, 2020. Originally scheduled to take place in Vienna, Austria, the symposium pivoted toward a fully online technical program in early March. As an in‐person event, SoCS offers participants a diverse array of scholarly activities including technical talks (long and short), poster...

The manipulation of articulated objects is of primary importance in Robotics and can be considered as one of the most complex manipulation tasks. Traditionally, this problem has been tackled by developing ad hoc approaches, which lack flexibility and portability. In this paper, we present a framework based on answer set programming (ASP) for the au...

In order to keep roads in acceptable condition, and to perform maintenance of essential infrastructure, roadworks are required. Due to the increasing traffic volumes and the increasing urbanisation, road agencies are currently facing the problem of effective planning frequent –and usually concurrent– roadworks in the controlled region. However, the...

Traffic congestion problems of urban road networks are having a strong impact on economy, due to losses from accidents and delays, and to public health. The recent progress in connected vehicles is expanding the approaches that can be exploited to tackle traffic congestion, particularly in urban regions. Connected vehicles pave the way to centralis...

This paper addresses two intertwined needs for collaborative robots operating in shop-floor environments. The first is the ability to perform complex manipulation operations, such as those on articulated or even flexible objects, in a way robust to a high degree of variability in the actions possibly carried out by human operators during collaborat...

In this paper we ask whether approximation for abstract argumentation is useful in practice, and in particular whether reasoning with grounded semantics – which has polynomial runtime – is already an approximation approach sufficient for several practical purposes. While it is clear from theoretical results that reasoning with grounded semantics is...

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 manipulation of articulated objects is of primary importance in Robotics, and can be considered as one of the most complex manipulation tasks. Traditionally, this problem has been tackled by developing ad-hoc approaches, which lack flexibility and portability. In this paper we present a framework based on Answer Set Programming (ASP) for the au...

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past Int. pioneering approaches, often fragmented in many disciplines,...

We investigate the computational problem of determining the set of acceptable arguments in abstract argumentation wrt. credulous and skeptical reasoning under grounded, complete, stable, and preferred semantics. In particular, we investigate the computational complexity of that problem and its verification variant, and develop four SAT-based algori...

The 13th Symposium on Combinatorial Search (SoCS) was held May 26-28, 2020. Originally scheduled to take place in Vienna, Austria, the symposium pivoted toward a fully online technical program in early March. As an in-person event SoCS offers participants a diverse array of scholarly activities including technical talks (long and short), poster ses...

The limited availability of resources makes the resource allocation strategy a pivotal aspect for every clinical department. Allocation is usually done on the basis of a workload estimation, which is performed by human experts. Experts have to dedicate a significant amount of time to the workload estimation, and the usefulness of estimations depend...

This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a...

This book presents a comprehensive review for Knowledge Engineering tools and techniques that can be used in Artificial Intelligence Planning and Scheduling. KE tools can be used to aid in the acquisition of knowledge and in the construction of domain models, which this book will illustrate. AI planning engines require a domain model which captur...

PESTLE analysis has been used for decades to help companies in taking challenging and complex decisions with regards to aspects such as the development of new lines of products, or the expansion into new markets. Despite its complexity, PESTLE analysis is still performed manually, with issues related to the efficiency of the overall process, and th...

The manipulation of articulated objects plays an important role in real-world robot tasks, both in home and industrial environments. A lot of attention has been devoted to the development of ad hoc approaches and algorithms for generating the sequence of movements the robot has to perform in order to manipulate the object. Such approaches can hardl...

It is well-known that the order in which clauses and literals are listed in a SAT formulae can have a strong impact on solvers’ performance.In this work we investigate how the performance of SAT solvers can be improved by a specifically-designed SAT formulae configuration. We introduce a fully automated approach for this configuration task, that co...

Automated planning techniques are increasingly exploited in real-world applications, thanks to their flexibility and robustness. Hybrid domains, those that require to reason both with discrete and continuous aspects, are particularly challenging to handle with existing planning approaches due to their complex dynamics. In this paper we present a ge...

In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated...

Recent advances in automated planning are leading towards the use of planning engines in a wide range of real-world applications. As the exploitation of planning techniques in applications increases, it becomes imperative to assess the robustness of planning engines with regards to poorly-engineered (or maliciously modified) knowledge models provid...

Argumentation is a prominent AI research area, focused on approaches and techniques for performing common-sense reasoning, that is of paramount importance in a wide range of real-world applications, such as decision support and recommender systems. In this work we introduce an approach for updating an abstract Argumentation Framework (AF) so that a...