From Space to Place and Back Again:Towards an Interface Between Space and Place

Geographic information systems represent and process space whereas people refer to and use place. A question that arises is what are the benefits of introducing a unified data model that combines the rigid representation of space and the information-rich concepts of place. In this work we contribute to this research question by proposing a two-way interface that aims to bridge the notions of space and place. This interface relies on the four conceptions of space and interconnected spatial objects. Step-by-step descriptions as well as examples are provided to illustrate the intended use of the proposed interface

Modal Logic S5 Satisfiability in Answer Set Programming

Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating satisfiability of S5 formulas commonly rely on Skolemisation to convert them into propositional logic formulas, essentially by introducing copies of propositional atoms for each set of interpretations (possible worlds). This approach is simple, but often results into large formulas that are too difficult to process, and therefore more parsimonious constructions are required. In this work, we propose to use Answer Set Programming for implementing such constructions, and in particular for identifying the propositional atoms that are relevant in every world by means of a reachability relation. The proposed encodings are designed to take advantage of other properties such as entailment relations of subformulas rooted by modal operators. An empirical assessment of the proposed encodings shows that the reachability relation is very effective and leads to comparable performance to a state-of-the-art S5 solver based on SAT, while entailment relations are possibly too expensive to reason about and may result in overhead.</p

From Space to Place and Back Again:Towards an Interface Between Space and Place

Geographic information systems represent and process space whereas people refer to and use place. A question that arises is what are the benefits of introducing a unified data model that combines the rigid representation of space and the information-rich concepts of place. In this work we contribute to this research question by proposing a two-way interface that aims to bridge the notions of space and place. This interface relies on the four conceptions of space and interconnected spatial objects. Step-by-step descriptions as well as examples are provided to illustrate the intended use of the proposed interface

A Generalised Approach for Encoding and Reasoning with Qualitative Theories in Answer Set Programming

Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number of specialised qualitative reasoning tools have been developed so far, an important barrier to the wider adoption of these tools is that only qualitative reasoning is supported natively, when real-world problems most often require a combination of qualitative and other forms of reasoning. In this work, we propose to overcome this barrier by using ASP as a unifying formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. These encodings are experimentally evaluated using a real-world dataset based on a case study of determining optimal coverage of telecommunication antennas, and compared with the performance of two well-known dedicated reasoners. Experimental results show that the proposed encodings outperform one of the two reasoners, but fall behind the other, an acceptable trade-off given the added benefits of handling any type of reasoning as well as the interpretability of logic programs. This paper is under consideration for acceptance in TPLP.Comment: Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 18 pages, 3 figure

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi. This manuscript is under consideration for acceptance in TPLP.Comment: Paper presented at the 34th International Conference on Logic Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018, 20 pages, LaTeX, 16 figure

A specification-based QoS-aware design framework for service-based applications

Effective and accurate service discovery and composition rely on complete specifications of service behaviour, containing inputs and preconditions that are required before service execution, outputs, effects and ramifications of a successful execution and explanations for unsuccessful executions. The previously defined Web Service Specification Language (WSSL) relies on the fluent calculus formalism to produce such rich specifications for atomic and composite services. In this work, we propose further extensions that focus on the specification of QoS profiles, as well as partially observable service states. Additionally, a design framework for service-based applications is implemented based on WSSL, advancing state of the art by being the first service framework to simultaneously provide several desirable capabilities, such as supporting ramifications and partial observability, as well as non-determinism in composition schemas using heuristic encodings; providing explanations for unexpected behaviour; and QoS-awareness through goal-based techniques. These capabilities are illustrated through a comparative evaluation against prominent state-of-the-art approaches based on a typical SBA design scenario

Artificial Intelligence Opportunities for Resilient Supply Chains

The need for supply chains to be resilient is increasingly being recognised, following recent disruptions caused by global socioeconomic crises. Supply chain resilience allows for sustainable growth and development through adaptive capabilities, principally including the ability to effectively respond to disruptions to maintain consistent operations. This paper explores the opportunities presented by Artificial Intelligence (AI) in enhancing supply chain resilience. We first conceptualise resilience through a 4-C model: context, capabilities, choices, and contingencies. We then explore a range of AI approaches and develop a research roadmap that attempts to map particular technologies holding potential to the 4-C model

Using MCDM Methods to Optimise Machine Learning Decisions for Supply Chain Delay Prediction:A Stakeholder-centric Approach

Background: This study addresses challenges faced by supply chain stakeholders who lack expert knowledge in making decisions related to Machine Learning. It introduces a novel use of Multi-Criteria Decision-Making as an evaluation mechanism for different classifiers, aiding stakeholders in selecting appropriate Machine Learning models for predicting supply chain delays.Methods: The proposed methodology involves applying classifiers (Decision Tree, Bagging, AdaBoost, Random Forest) and evaluating them using quantitative and qualitative metrics. MCDM methods (TOPSIS, MARCOS, COCOSO, MABAC) rank these Machine Learning models, facilitating accessible decision-making for stakeholders. A pharmaceutical industry case study is employed to validate the approach, utilizing Python for analysis.Results: Case study results confirm the effectiveness of the proposed approach, combining Multi-Criteria Decision-Making with Machine Learning in order to facilitate stakeholder decisions on suitable algorithms for predicting supply chain delays. The Random Forest classifier is identified as the most balanced option in the context of the case study and clear rationale can be drawn in support or against each option through metrics comparison, validating the approachs practical applicability and effectiveness.Conclusions: The combination of Multi-Criteria Decision-Making with Machine Learning provides a significant advancement in empowering stakeholders in supply chain management, particularly those lacking in-depth Machine Learning expertise. This approach enhances decision-making in model selection, with the potential of improving supply chain efficiency as a result