A consequence of a logic program is a consistent set of literals that are satisfied by every answer set. The well-founded model is a consequence that can be used to simplify the logic program. In this paper, we extend the notion of well-founded models to consequences for simplifying disjunctive logic programs (DLPs) in a general manner. Specifically, we provide two main notions, strong reliable set and weak reliable set, and show that a DLP is strongly equivalent to the simplified program if and only if the consequence is a strong reliable set, and they have the same answer sets if and only if the consequence is a weak reliable set. Then we provide computational complexity on identifying both notions. In addition, we provide an algorithm to compute some strong reliable sets and show that the approach is an extension of the well-founded model in simplifying logic programs.

Belief revision is concerned with incorporating new information into a pre-existing set of beliefs. When the new information comes from another agent, we must first determine if that agent should be trusted. In this paper, we define trust as a pre-processing step before revision. We emphasize that trust in an agent is often restricted to a particular domain of expertise. We demonstrate that this form of trust can be captured by associating a state partition with each agent, then relativizing all reports to this partition before revising.  We position the resulting family of trust-sensitive revision operators within the class of selective revision operators of Ferme and Hansson, and we examine its properties. In particular, we show how trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information.  When multiple reporting agents are involved, we use a distance function over states to represent differing degrees of trust; this ensures that the most trusted reports will be believed.

Computational models of argument could play a valuable role in persuasion technologies for behaviour change (e.g. persuading a user to eat a more healthy diet, or to drink less, or to take more exercise, or to study more conscientiously, etc). For this, the system (the persuader) could present arguments to convince the user (the persuadee). In this paper, we consider asymmetric dialogues where only the system presents arguments, and the system maintains a model of the user to determine the best choice of arguments to present (including counterarguments to key arguments believed to be held by the user). The focus of the paper is on the user model, including how we update it as the dialogue progresses, and how we use it to make optimal choices for dialogue moves.

Schema.org is an initiative by the major search engine providers Bing, Google, Yahoo, and Yandex that provides a collection of ontologies which webmasters can use to mark up their pages. Schema.org comes without a formal language definition and without a clear semantics. We formalize the language of Schema.org as a Description Logic (DL) and study the complexity of querying data using (unions of) conjunctive queries in the presence of ontologies formulated in this DL (from several perspectives). While querying is intractable in general, we identify various cases in which it is tractable and where queries are even rewritable into FO queries or datalog programs.

Belief merging is a central operation within the field of belief change and addresses the problem of combining multiple, possibly mutually inconsistent knowledge bases into a single, consistent one. A current research trend in belief change is concerned with tailored representation theorems for fragments of logic, in particular Horn logic. Hereby, the goal is to guarantee that the result of the change operations stays within the fragment under consideration. While several such results have been obtained for Horn revision and Horn contraction, merging of Horn theories has been neglected so far. In this paper, we provide a novel representation theorem for Horn merging by strengthening the standard merging postulates. Moreover, we present a concrete Horn merging operator satisfying all postulates.

We propose a new type of algorithm for computing first-order (FO) rewritings of concept queries under ELHdr-TBoxes. The algorithm is tailored towards efficient implementation, yet complete. It outputs a succinct non-recursive datalog rewriting if the input is FO-rewritable and otherwise reports non-FO-rewritability. We carry out experiments with real-world ontologies which demonstrate excellent performance in practice and show that TBoxes originating from applications admit FO-rewritings of reasonable size in almost all cases, even when in theory such rewritings are not guaranteed to exist.

However, as is well known, the but-for test is not always sufficient to determine causality. Consider the following The original Halpern-Pearl definition of causality well-known example, taken from [Paul and Hall, 2013]: [Halpern and Pearl, 2001] was updated in the journal Suzy and Billy both pick up rocks and throw them version of the paper [Halpern and Pearl, 2005] at a bottle. Suzy's rock gets there first, shattering to deal with some problems pointed out by Hopkins the bottle. Since both throws are perfectly accurate, and Pearl [2003]. Here the definition is modified Billy's would have shattered the bottle had it not yet again, in a way that (a) leads to a simpler definition, been preempted by Suzy's throw.

We study temporal description logics (TDLs) based on the branching-time temporal logic CTL and the lightweight DL EL in the presence of rigid roles and restricted TBoxes. While TDLs designed in this way are known to be inherently nonelementary or even undecidable over general TBoxes, there is hope for a better computational behaviour over acyclic or empty TBoxes. We begin by showing that the basic DL ALC combined with CTL in the described way is indeed decidable, but still inherently nonelementary. As our main contribution, we identify several TDLs of elementary complexity, obtained by combining EL with CTL fragments that allow only restricted sets of temporal operators. We obtain upper complexity bounds ranging from PTime to coNExpTime and mostly tight lower bounds. This contrasts the fact that the respective ALC variants are already inherently nonelementary.

We study a general class of multiagent optimization problems, together with a compact representation language of utilities based on weighted propositional formulas. We seek solutions maximizing utilitarian social welfare as well as fair solutions maximizing the utility of the least happy agent. We show that many problems can be expressed in this setting, such as fair division of indivisible goods, some multiwinner elections, or multifacility location. We focus on the complexity of finding optimal solutions, and we identify the tractability boarder between polynomial and NP-hard settings, along several parameters: the syntax of formulas, the allowed weights, as well as the number of agents, propositional symbols, and formulas per agent.

We consider the scenario of ontology-based query answering. It is generally accepted that true scalability in this setting can only be achieved via query rewriting, which in turn allows for the exploitation of standard RDBMSs. In this work, we close two open fundamental questions related to query rewriting. We establish that linear existential rules are polynomially combined rewritable, while full linear rules are polynomially (purely) rewritable; in both cases, the target query language consists of first-order or non-recursive Datalog queries. An immediate consequence of our results is that DLR-Lite_R, the extension of DL-Lite_R with n-ary roles, is polynomially combined rewritable.