The tutorials presented at the Fourteenth International Conference on Principles of Knowledge Representation and Reasoning included Verification of Multi-Agent Systems against Epistemic Specifications by Alessio Lomuscio, Dynamic Epistemic Logic and Its Interaction with Knowledge Representation by Lawrence S. Moss, Natural Language Understanding with World Knowledge and Inference by Ekaterina Ovchinnikova, and Query Answering and Rewriting in Ontology-Based Data Access by Riccardo Rosati.

This paper presents Tweety, an open source project for scientific experimentation on logical aspects of artificial intelligence and particularly knowledge representation. Tweety provides a general framework for implementing and testing knowledge representation formalisms in a way that is familiar to researchers used to logical formalizations. This framework is very general, widely applicable, and can be used to implement a variety of knowledge representation formalisms from classical logics, over logic programming and computational models for argumentation, to probabilistic modeling approaches. Tweety already contains over 15 different knowledge representation formalisms and allows easy computation of examples, comparison of algorithms and approaches, and benchmark tests. This paper gives an overview on the technical architecture of Tweety and a description of its different libraries. We also provide two case studies that show how Tweety can be used for empirical evaluation of different problems in artificial intelligence.

The invited talks presented at the Fourteenth International Conference on Principles of Knowledge Representation and Reasoning included Ontology-Based Monitoring of Dynamic Systems by Franz Baader, Knowledge Representation Meets Computer Vision: From Pixels to Symbolic Activity Descriptions by Tony Cohn, Datalog+/–: Questions and Answers by Georg Gottlob, and Situation Calculus: The Last 15 Years by Sheila McIlraith.

In Description Logic (DL) knowledge bases (KBs) information is typically captured by crisp concepts. For many applications, querying the KB by crisp query concepts is too restrictive. A controlled way of gradually relaxing a query concept can be achieved by the use of concept similarity measures. In this paper we formalize the task of instance query answering for crisp DL KBs using concepts relaxed by concept similarity measures. We investigate computation algorithms for this task in the DL EL, their complexity and properties for the employed similarity measure regarding whether unfoldable or general TBoxes are used.

Today's propositional satisfiability (SAT) solvers are extremely powerful and can be used as an efficient back-end for solving NP-complete problems. However, many fundamental problems in knowledge representation and reasoning are located at the second level of the Polynomial Hierarchy or even higher, and hence polynomial-time transformations to SAT are not possible, unless the hierarchy collapses. Recent research shows that in certain cases one can break through these complexity barriers by fixed-parameter tractable (fpt) reductions which exploit structural aspects of problem instances in terms of problem parameters. In this paper we develop a general theoretical framework that supports the classification of parameterized problems on whether they admit such an fpt-reduction to SAT or not. We instantiate our theory by classifying the complexities of several case study problems, with respect to various natural parameters. These case studies include the consistency problem for disjunctive answer set programming and a robust version of constraint satisfaction.

List of sponsors for the Fourteenth International Conference on Principles of Knowledge Representation and Reasoning.

List of conference committee members for the Fourteenth International Conference on Principles of Knowledge Representation and Reasoning.

We present a cognitive design assistance system equipped with analytical capabilities aimed at anticipating architectural building design performance with respect to people-centred functional design goals. The paper focuses on the system capability to generate "narratives of visuo-locomotive user experience" from digital computer-aided architecture design (CAAD) models. The system is based on an underlying declarative narrative representation and computation framework pertaining to conceptual, geometric, and qualitative spatial knowledge. The semantics of the declarative narrative model, i.e., the overall   representation and computation model, is founded on: (a). conceptual knowledge formalised in an OWL ontology; (b). a general spatial representation and reasoning engine implemented in constraint logic programming; and (c). a declaratively encoded (narrative) construction process (based on search over graph structures) implemented in answer-set programming. We emphasise and demonstrate: complete system implementation, scalability, and robust performance & integration with industry-scale architecture industry tools (e.g., Revit, ArchiCAD) & standards (BIM, IFC).

We reduce a broad class of machine learning problems, usually addressed by EM or sampling, to the problem of finding the $k$ extremal rays spanning the conical hull of a data point set. These $k$ "anchors" lead to a global solution and a more interpretable model that can even outperform EM and sampling on generalization error. To find the $k$ anchors, we propose a novel divide-and-conquer learning scheme "DCA" that distributes the problem to $\mathcal O(k\log k)$ same-type sub-problems on different low-D random hyperplanes, each can be solved by any solver. For the 2D sub-problem, we present a non-iterative solver that only needs to compute an array of cosine values and its max/min entries. DCA also provides a faster subroutine for other methods to check whether a point is covered in a conical hull, which improves algorithm design in multiple dimensions and brings significant speedup to learning. We apply our method to GMM, HMM, LDA, NMF and subspace clustering, then show its competitive performance and scalability over other methods on rich datasets.

Heuristic search planning effectively finds solutions for large planning problems, but since the estimates are either not admissible or too weak, optimal solutions are found in rare cases only. In contrast, heuristic pattern databases are known to significantly improve lower bound estimates for optimally solving challenging single-agent problems like the 24-Puzzle or Rubik’s Cube. This paper studies the effect of pattern databases in the context of deterministic planning. Given a fixed state description based on instantiated predicates, we provide a general abstraction scheme to automatically create admissible domain-independent memory-based heuristics for planning problems, where abstractions are found in factorizing the planning space. We evaluate the impact of pattern database heuristics in A* and hill climbing algorithms for a collection of benchmark domains.