52 research outputs found
Answer Set Programming for Qualitative Spatio-Temporal Reasoning: Methods and Experiments
We study the translation of reasoning problems involving qualitative spatio-temporal calculi into answer set programming (ASP). We present various alternative transformations and provide a qualitative comparison among them. An implementation of these transformations is provided by a tool that transforms problem instances specified in the language of the Generic Qualitative Reasoner (GQR) into ASP problems. Finally, we report on an experimental analysis of solving consistency problems for Allen\u27s Interval Algebra and the Region Connection Calculus with eight base relations (RCC-8)
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
Representing time and space for the semantic web
Representation of temporal and spatial information for the Semantic Web often involves qualitative defined information (i.e., information described using natural language terms such as "before" or "overlaps") since precise dates or coordinates are not always available. This work proposes several temporal representations for time points and intervals and spatial topological representations in ontologies by means of OWL properties and reasoning rules in SWRL. All representations are fully compliant with existing Semantic Web standards and W3C recommendations. Although qualitative representations for temporal interval and point relations and spatial topological relations exist, this is the first work proposing representations combining qualitative and quantitative information for the Semantic Web. In addition to this, several existing and proposed approaches are compared using different reasoners and experimental results are presented in detail. The proposed approach is applied to topological relations (RCC5 and RCC8) supporting both qualitative and quantitative (i.e., using coordinates) spatial relations. Experimental results illustrate that reasoning performance differs greatly between different representations and reasoners. To the best of our knowledge, this is the first such experimental evaluation of both qualitative and quantitative Semantic Web temporal and spatial representations. In addition to the above, querying performance using SPARQL is evaluated. Evaluation results demonstrate that extracting qualitative relations from quantitative representations using reasoning rules and querying qualitative relations instead of directly querying quantitative representations increases performance at query time
Answer Set Programming for Qualitative Spatio-temporal Reasoning: Methods and Experiments
We study the translation of reasoning problems involving qualitative spatio-temporal calculi into answer set programming (ASP). We present various alternative transformations and provide a qualitative comparison among them. An implementation of these transformations is provided by a tool that transforms problem instances specified in the language of the Generic Qualitative Reasoner (GQR) into ASP problems.
Finally, we report on an experimental analysis of solving consistency problems for Allen’s Interval
Algebra and the Region Connection Calculus with eight base relations (RCC-8)
Exploiting Parallelism for Hard Problems in Abstract Argumentation
Abstract argumentation framework (AF) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to AF s are missing, thus potentially limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup
Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK
Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learning model and a knowledge-based model) for assisting adult ADHD diagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future
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 survey of large-scale reasoning on the Web of data
As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning
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