In this article, we report on a multiphase R&D effort to develop a conversational second screen application for TV program discovery. Our goal is to share with the community the breadth of artificial intelligence (AI) and natural language (NL) technologies required to develop such an application along with learnings from target end-users. We first give an overview of our application from the perspective of the end-user. We then present the architecture of our application along with the main AI and NL components, which were developed over multiple phases. The first phase focuses on enabling core functionality such as effectively finding programs matching the user’s intent. The second phase focuses on enabling dialog with the user. Finally, we present two user studies, corresponding to these two phases. The results from both studies demonstrate the effectiveness of our application in the target domain.

The AAAI 2015 Spring Symposium Series was held Monday through Wednesday, March 23-25, at Stanford University near Palo Alto, California. The titles of the seven symposia were Ambient Intelligence for Health and Cognitive Enhancement, Applied Computational Game Theory, Foundations of Autonomy and Its (Cyber) Threats: From Individuals to Interdependence, Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Logical Formalizations of Commonsense Reasoning, Socio-Technical Behavior Mining: From Data to Decisions, Structured Data for Humanitarian Technologies: Perfect Fit or Overkill? and Turn-Taking and Coordination in Human-Machine Interaction.The highlights of each symposium are presented in this report.

Higher-level cognition includes logical reasoning and the ability of question answering with common sense. The RatioLog project addresses the problem of rational reasoning in deep question answering by methods from automated deduction and cognitive computing. In a first phase, we combine techniques from information retrieval and machine learning to find appropriate answer candidates from the huge amount of text in the German version of the free encyclopedia "Wikipedia". In a second phase, an automated theorem prover tries to verify the answer candidates on the basis of their logical representations. In a third phase - because the knowledge may be incomplete and inconsistent -, we consider extensions of logical reasoning to improve the results. In this context, we work toward the application of techniques from human reasoning: We employ defeasible reasoning to compare the answers w.r.t. specificity, deontic logic, normative reasoning, and model construction. Moreover, we use integrated case-based reasoning and machine learning techniques on the basis of the semantic structure of the questions and answer candidates to learn giving the right answers.

The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what intelligent systems need for successfully interacting with the physical world, one of the grand challenges of AI. As such the competition offers a simplified and controlled environment for developing and testing the necessary AI technologies, a seamless integration of computer vision, machine learning, knowledge representation and reasoning, reasoning under uncertainty, planning, and heuristic search, among others. Over the past three years there have been significant improvements, but we are still a long way from reaching the ultimate aim and, thus, there are great opportunities for participants in this competition.

Although a number of initiatives provide personalized context-aware guidance for niche use-cases, a standard framework for context awareness remains lacking. This article explains how semantic technology has been exploited to generate a centralized repository of personal activity context. This data drives advanced features such as, personal situation recognition and customizable rules for the context-sensitive management of personal devices and data sharing. As a proof-of-concept, we demonstrate how an innovative context-aware system has successfully adopted such an infrastructure.

The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what intelligent systems need for successfully interacting with the physical world, one of the grand challenges of AI. As such the competition offers a simplified and controlled environment for developing and testing the necessary AI technologies, a seamless integration of computer vision, machine learning, knowledge representation and reasoning, reasoning under uncertainty, planning, and heuristic search, among others. Over the past three years there have been significant improvements, but we are still a long way from reaching the ultimate aim and, thus, there are great opportunities for participants in this competition.

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding a longest simple path in a graph, maximal coverage, and various constraint optimization problems. We examine several popular search algorithms for MIN problems and discover the curious ways in which they misbehave on MAX problems. We propose modifications that preserve the original intentions behind the algorithms but allow them to solve MAX problems, and compare them theoretically and empirically. Interesting results include the failure of bidirectional search and close relationships between Dijkstra's algorithm, weighted A*, and depth-first search.

The classical theory assumes that each concept is represented by a set of features In this thesis, I investigate a hybrid knowledge representation that are shared by all the instances that are abstracted by approach that combines classic knowledge the concept. In this way, concepts can be viewed as rules representations, such as rules and ontologies, for classifying objects based on features. The prototype theory, with other cognitively plausible representations, on the other hand, states that concepts are represented such as prototypes and exemplars. The resulting through a typical instance, which has the typical features of framework can combine the strengths of the instances of the concept. Finally, the exemplar theory assumes each approach of knowledge representation, avoiding that each concept is represented by a set of exemplars their weaknesses. It can be used for developing of it. These exemplars are real entities that were previously knowledge-based systems that combine logicbased experienced by the agent. In theories based on prototypes or reasoning and similarity-based reasoning in exemplars, the categorization of a given entity is performed problem-solving processes.

The standard way of answering queries over incomplete databases is to compute certain answers, defined as the intersection of query answers on all complete databases that the incomplete database represents. But is this universally accepted definition correct? We argue that this ``one-size-fits-all'' definition can often lead to counterintuitive or just plain wrong results, and propose an alternative framework for defining certain answers. We combine three previously used approaches, based on the semantics and representation systems, on ordering incomplete databases in terms of their informativeness, and on viewing databases as knowledge expressed in a logical language, to come up with a well justified and principled notion of certain answers. Using it, we show that for queries satisfying some natural conditions (like not losing information if a more informative input is given), computing certain answers is surprisingly easy, and avoids the complexity issues that have been associated with the classical definition.

The computation of minimal conflict sets is a central task when the goal is to find relaxations or explanations for overconstrained problem formulations and in particular in the context of Model-Based Diagnosis (MBD) approaches. In this paper we propose MergeXPlain, a non-intrusive conflict detection algorithm which implements a divide-and-conquer strategy to decompose a problem into a set of smaller independent subproblems. Our technique allows us to efficiently determine multiple minimal conflicts during one single problem decomposition run, which is particularly helpful in MBD problem settings. An empirical evaluation on various benchmark problems shows that our method can lead to a significant reduction of the required diagnosis times.