Answer Set Programming Modulo `Space-Time'

We present ASP Modulo `Space-Time', a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of `space-time' regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks

A cookbook for temporal conceptual data modelling with description logic

We design temporal description logics suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models

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

Concept Learning with Energy-Based Models

Many hallmarks of human intelligence, such as generalizing from limited experience, abstract reasoning and planning, analogical reasoning, creative problem solving, and capacity for language require the ability to consolidate experience into concepts, which act as basic building blocks of understanding and reasoning. We present a framework that defines a concept by an energy function over events in the environment, as well as an attention mask over entities participating in the event. Given few demonstration events, our method uses inference-time optimization procedure to generate events involving similar concepts or identify entities involved in the concept. We evaluate our framework on learning visual, quantitative, relational, temporal concepts from demonstration events in an unsupervised manner. Our approach is able to successfully generate and identify concepts in a few-shot setting and resulting learned concepts can be reused across environments. Example videos of our results are available at sites.google.com/site/energyconceptmodel

Temporal Representation and Reasoning in OWL 2

The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time (referred to as “dynamic” information) and of their properties in terms of qualitative descriptions in addition to quantitative ones (i.e., dates, time instants and intervals). For this purpose, we advocate the use of natural language expressions, such as “before” or “after”, for temporal entities whose exact durations or starting and ending points in time are unknown. Reasoning over all types of temporal information (such as the above) is also an important research problem. The current work addresses all these issues as follows: The representation of dynamic concepts is achieved using the “4D-fluents” or, alternatively, the “N-ary relations” mechanism. Both mechanisms are thoroughly explored and are expanded for representing qualitative and quantitative temporal information in OWL. In turn, temporal information is expressed using either intervals or time instants. Qualitative temporal information representation in particular, is realized using sets of SWRL rules and OWL axioms leading to a sound, complete and tractable reasoning procedure based on path consistency applied on the existing relation sets. Building upon existing Semantic Web standards (OWL), tools and member submissions (SWRL), as well as integrating temporal reasoning support into the proposed representation, are important design features of our approach

Representation of Temporal Relationship Among Events

Representation and Reasoning about a temporal information became a crucial aspect for many of the natural language process applications namely question answering system temporal summarization etc., Temporal information exists majorly in two forms i.e either in in the form of qualitative nature or quantitative. Temporal information extraction is vital to project the relation among the events that occur in any scenario. This information intern enables to answer queries about date, duration and other temporal nature of events. This paper focus on automatic extraction and representation of temporal relation among the events. Few attributes of TIMEML tags namely and are used to retrieve temporal information among events. Reasoning was applied to retrieve the unknown information from the known ones. Experiments were conducted on TIMEML corpus and the results obtained from the experiments were found to be encouraging

Equilibria-based Probabilistic Model Checking for Concurrent Stochastic Games

Probabilistic model checking for stochastic games enables formal verification of systems that comprise competing or collaborating entities operating in a stochastic environment. Despite good progress in the area, existing approaches focus on zero-sum goals and cannot reason about scenarios where entities are endowed with different objectives. In this paper, we propose probabilistic model checking techniques for concurrent stochastic games based on Nash equilibria. We extend the temporal logic rPATL (probabilistic alternating-time temporal logic with rewards) to allow reasoning about players with distinct quantitative goals, which capture either the probability of an event occurring or a reward measure. We present algorithms to synthesise strategies that are subgame perfect social welfare optimal Nash equilibria, i.e., where there is no incentive for any players to unilaterally change their strategy in any state of the game, whilst the combined probabilities or rewards are maximised. We implement our techniques in the PRISM-games tool and apply them to several case studies, including network protocols and robot navigation, showing the benefits compared to existing approaches