Belief revision studies strategies about how agents revise their belief states when receiving new evidence.  Both in classical belief revision and in epistemic revision, a new input is either in the form of a (weighted) propositional formula or a total pre-order (where the total pre-order is considered as a whole).  However, in some real-world applications, a new input can be a partial pre-order where each unit that constitutes the partial pre-order is important and should be considered individually. To address this issue,  in this paper, we study how a partial pre-order representing the prior epistemic state can be revised by another partial pre-order (the new input) from a different perspective, where the revision is conducted recursively on the individual units of partial pre-orders.  We propose different revision operators (rules), dubbed the extension, match, inner and outer revision operators, from different revision points of view. We also analyze several properties for these operators.

This paper presents a characterisation of and definitions for the phenomenon of masking in the context of justifications for entailments in ontologies. In essence masking is present within a justification, over a set of justifications, or over a complete ontology when the number of justifications for an entailment does not reflect the number of reasons for that entailment. Four types of masking are defined in this paper: Internal Masking, Cross Masking, External Masking and Shared Core Masking. The results of an empirical study are presented which shows that the phenomenon of masking is prevalent throughout ontologies with non-trivial entailments in the NCBO BioPortal corpus. Out of 72 ontologies, 53 exhibited some form of masking, with 9 ontologies exhibiting internal masking, 23 ontologies exhibiting external masking, and 53 ontologies exhibiting shared core masking.

Temporal logics can be used in reasoning about actions for specifying constraints on domain descriptions and temporal properties to be verified. In this paper, we exploit Bounded Model Checking (BMC) techniques in the verification of Dynamic Linear Time Temporal Logic (DLTL) properties of an action theory, which is formulated in a temporal extension of Answer Set Programming (ASP). To achieve completeness, we propose an approach to BMC which exploits the Buechi automaton construction while searching for a counterexample. We provide an encoding in ASP of the temporal action domain and of Bounded Model Checking of DLTL formulas.

The advance of Internet and Sensor technology has brought about new challenges evoked by the emergence of continuous data streams. While existing data-stream management systems allow for high-throughput stream processing, they lack complex reasoning capacities. We address this shortcoming and elaborate upon an approach to knowledge-intense stream reasoning based on Answer Set Programming (ASP). The emphasis thus shifts from rapid data processing to complex reasoning. To accommodate this in ASP, we develop new techniques that allow us to formulate problem encodings dealing with emerging as well as expiring data in a seamless way. We thus propose novel language constructs and modeling techniques for specifying and reasoning with time-decaying logic programs.

Belief merging aims at extracting a coherent and informative view from a set of belief bases. A first requirement for belief merging operators is to obey basic rationality conditions. Another expected property is to preserve as much information as possible from the input bases. In this paper, we show how new merging operators, called compositional operators, can be defined from existing ones. Such operators aim at offering a higher discriminative power than the merging operators on which they are based, without leading to a complexity shift or losing rationality postulates. We identify some sufficient conditions for ensuring that rationality is fully preserved by composition.

We describe the application of assumption-based argumentation (ABA) to a domain of medical knowledge derived from clinical trials of drugs for breast cancer. We adapt an algorithm for calculating the admissible semantics for ABA frameworks to take account of preferences and describe a prototype implementation which uses variant-based parallel computation to improve the efficiency of query answering.

Recently, (Dunne et al. 2009; 2011) have suggested to weight attacks within Dung’s abstract argumentation frameworks, and introduced the concept of WAF (Weighted Argumentation Framework). However, they use WAFs in a very specific way for relaxing attacks. The aim of this paper is to explore ways to take advantage of attacks weights within an argumentation process. Two different approaches are considered: The first one extends the proposal by (Dunne et al. 2011) and accounts for other aggregation functions than sum in the objective of relaxing attacks. The second one shows how weights can be exploited to strengthen the usual notion of defence, leading to new concepts of extensions.

Successful analysis of video data requires an integration of techniques from KR, Computer Vision, and Machine Learning. Being able to detect and to track objects as well as extracting their changing spatial relations with other objects is one approach to describing and detecting events. Different kinds of spatial relations are important, including topology, direction, size, and distance between objects as well as changes of those relations over time. Typically these kinds of relations are treated separately, which makes it difficult to integrate all the extracted spatial information. We present a uniform and comprehensive spatial representation of moving objects that includes all the above spatial/temporal aspects, analyse different properties of this representation and demonstrate that it is suitable for video analysis.

In order to meet usability requirements, most logic-based applications provide explanation facilities for reasoning services. This holds also for DLs, where research has focused on the explanation of both TBox reasoning and, more recently, query answering. Besides explaining the presence of a tuple in a query answer, it is important to explain also why a given tuple is missing. We address the latter problem for (conjunctive) query answering over DL-Lite ontologies, by adopting abductive reasoning, that is, we look for additions to the ABox that force a given tuple to be in the result. As reasoning taskswe consider existence and recognition of an explanation, and relevance and necessity of a certain assertion for an explanation. We characterize the computational complexity of these problems for subset minimal and cardinality minimal explanations.

We present a system capable of automatically solving combinatorial logic puzzles given in (simplified) English. It uses an ontology to represent the puzzles in ASP which is applicable to a large set of logic puzzles. To translate the English descriptions of the puzzles into this ontology, we use a lambda-calculus based approach using Probabilistic Combinatorial Categorial Grammars (PCCG) where the meanings of words are associated with parameters to be able to distinguish between multiple meanings of the same word.