In this paper we propose a novel method for image semantic segmentation using multiple graphs. The multiview affinity graph is constructed by leveraging the consistency between semantic space and multiple visualspaces. With block-diagonal constraints, we enforce the affinity matrix to be sparse such that the pairwise potential for dissimilar superpixels is close to zero. By a divide-and-conquer strategy, the optimizationfor learning affinity matrix is decomposed into several subproblems that can be solved in parallel. Using the neighborhood relationship between superpixels and the consistency between affinity matrix and labelconfidencematrix, we infer the semantic label for each superpixel of unlabeled images by minimizing an objective whose closed form solution can be easily obtained. Experimental results on two real-world image datasetsdemonstrate the effectiveness of our method.

Being able to quickly and naturally teach robots new knowledge is critical for many future open-world human-robot interaction scenarios. In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object. We describe the architectural components and demonstrate the proposed approach on a robotic platform in a proof-of-concept evaluation.

SenticNet is a publicly available semantic and affective resource for concept-level sentiment analysis. Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of "energy flows" to connect various parts of extended common and common-sense knowledge representations to one another. SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and affective information associated with multi-word natural language expressions), representing information with a symbolic opacity of an intermediate nature between that of neural networks and typical symbolic systems.

A large number of interdependent issues in complex contract negotiation poses a significant challenge for current approaches, which becomes even more apparent when negotiation problems scale up. To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator. This structured negotiation process increases computational efficiency, making negotiations scalable for large number of interdependent issues. To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. The experimental result supports the hypothesis that this hierarchical negotiation approach greatly improves scalability with the complexity of the negotiation scenarios.

We introduce Dramatis, a computational model of suspense based on a reformulation of a psychological definition of the suspense phenomenon. In this reformulation, suspense is correlated with the audience’s ability to generate a plan for the protagonist to avoid an impending negative outcome. Dramatis measures the suspense level by generating such a plan and determining its perceived likelihood of success. We report on three evaluations of Dramatis, including a comparison of Dramatis output to the suspense reported by human readers, as well as ablative tests of Dramatis components. In these studies, we found that Dramatis output corresponded to the suspense ratings given by human readers for stories in three separate domains.

Therefore, diagnostic algorithms must reason backwards from symptoms to causes. For example, determining that a dead battery is the cause of your car not starting in the morning (and not the wiring or the ignition switch). The domains of diagnostic algorithms includes analog and digital circuits, software systems, thermal systems, biological systems, and physical mechanisms. The same classes of diagnostic algorithms can apply in all domains. Diagnostic algorithms make observations, often in real time, of a system being diagnosed.

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.

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).

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.