We analyze the patterns of autistic reasoning while performing planning tasks. The formalism of non-monotonic logic of defaults is used to simulate the autistic decision-making while adjusting an action to a context. Our current main finding is that while people with autism may be able to process single default rules, they have a characteristic difficulty in cases where multiple default rules conflict. Even though default reasoning was intended to simulate the reasoning of typical human subjects, it turns out that following the operational semantics of default reasoning in a literal way leads to the peculiarities of autistic behavior observed in the literature.

Ontologies are not only becoming a widespread formalism to create the knowledge base of current intelligent and semantic systems, but they are also suitable for modeling context information in ubiquitous applications, which require expressive representation and reasoning languages. In this paper, we discuss different approaches for ontological context management, as well as a proposal to represent and exploit significance-based relations with standard and fuzzy ontologies.

Mobile applications can be greatly enhanced if they have information about the situation of the user. Situations may be inferred by analyzing several types of contextual information drawn from device sensors, such as location, motion, ambiance and proximity. To capture a richer understanding of users’ situations, we introduce an ontology describing the relations between background knowledge about the user and contexts inferred from sensor data. With the right combination of machine learning and semantic modeling, it is possible to create high-level interpretations of user behaviors and situations. However, the potential of understanding and interpreting behavior with such detailed granularity poses significant threats to personal privacy. We propose a framework to mitigate privacy risks by filtering sensitive data in a context-aware way, and maintain provenance of inferred situations as well as relations between existing contexts when sharing information with other parties.

Introspection is a question-led process in which one builds on what one already knows to explore what is possible and plausible. In creative introspection, whether in art or in science, framing the right question is as important as finding the right answer. Presupposition-laden questions are themselves a source of knowledge, and in this paper we show how widely-held beliefs about the world can be dynamically acquired by harvesting such questions from the Web. We show how metaphorical reasoning can be modeled as an introspective process, one that builds on questions harvested from the Web to pose further speculative questions and queries. Metaphor is much more than a knowledge-hungry rhetorical device: it is a conceptual lever that allows a system to extend its model of the world.

We consider a new class of planning problems involving a set of non-negative real variables, and a set of non-deterministic actions that increase or decrease the values of these variables by some arbitrary amount. The formulas specifying the initial state, goal state, or action preconditions can only assert whether certain variables are equal to zero or not. Assuming that the state of the variables is fully observable, we obtain two results. First, the solution to the problem can be expressed as a policy mapping qualitative states into actions, where a qualitative state includes a Boolean variable for each original variable, indicating whether its value is zero or not. Second, testing whether any such policy, that may express nested loops of actions, is a solution to the problem, can be determined in time that is polynomial in the qualitative state space, which is much smaller than the original infinite state space. We also report experimental results using a simple generate-and-test planner to illustrate these findings.

General Game Players (GGPs) are programs which can play an arbitrary game given only its rules and the Game Description Language (GDL) is a variant of Datalog used in GGP competitions to specify the rules. GDL inherits from Datalog the use of Horn clauses as rules and recursion, but it too requires stratification and does not allow to use quantifiers. We present an alternative formalism for game description which is based on first-order logic (FO). States of the game are represented by relational structures, legal moves by structure rewriting rules guarded by FO formulas, and the goals of the players by formulas which extend FO with counting. The advantage of our formalism comes from more explicit state representationcand from the use of quantifiers in formulas. We show how to exploit existential quantification in players' goals to generate heuristics for evaluating positions in the game. The derived heuristics are good enough for a basic alpha-beta agent to win against state of the art GGP.

We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed subproblems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms show that CCRank gains in both accuracy and efficiency.

The memory requirements of basic best-first heuristic search algorithms like A* make them infeasible for solving large problems. External disk storage is cheap and plentiful com- pared to the cost of internal RAM. Unfortunately, state-of- the-art external memory search algorithms either rely on brute-force search techniques, such as breadth-first search, or they rely on all node values falling in a narrow range of in- tegers, and thus perform poorly on real-world domains with real-valued costs. We present a new general-purpose algo- rithm, PEDAL, that uses external memory and parallelism to perform a best-first heuristic search capable of solving large problems with real costs. We show theoretically that PEDAL is I/O efficient and empirically that it is both better on a stan- dard unit-cost benchmark, surpassing internal IDA* on the 15-puzzle, and gives far superior performance on problems with real costs.

Empirical findings indicate that humans draw infer- ences about spatial arrangements by constructing and manipulating mental models which are internal representations of objects and relations in spatial working memory. Central to the Mental Model Theory (MMT), is the assumption that the human reasoning process can be divided into three phases: (i) Mental model construction, (ii) model inspection, and (iii) model validation. The MMT can be formalized with respect to a computational model, connecting the reasoning process to operations on mental model representations. In this respect a computational model has been implemented in the cognitive architecture ACT-R capable of explaining human reasoning difficulty by the number of model operations. The presented ACT-R model allows simulation of psychological findings about spatial reasoning problems from a previous study that investigated conventional behavioral data such as response times and error rates in the context of certain mental model construction principles.

Description Logics (DLs) provide a clear and broadly accepted paradigm for modeling and reasoning about terminological knowledge. However, it has been often noted, that although DLs are well-suited for representing a single, global viewpoint on an application domain, they offer no formal grounding for dealing with knowledge pertaining to multiple heterogeneous viewpoints — a scenario ever more often approached in practical applications, e.g. concerned with reasoning over distributed knowledge sources on the Semantic Web. In this paper, we study a natural extension of DLs, in the style of two-dimensional modal logics, which supports declarative modeling of viewpoints as contexts, in the sense of McCarthy, and their semantic interoperability. The formalism is based on two-dimensional semantics, where one dimension represents a usual object domain and the other a (possibly infinite) domain of viewpoints, addressed by additional modal operators and a metalanguage, on the syntactic level. We systematically introduce a number of expressive fragments of the proposed logic, study their computational complexity and connections to related formalisms.