This paper describes a prototype system that generates mathematical word problems from ontologies in unrestricted domains. It builds on an existing ontology verbaliser that renders logical statements written in Web Ontology Language (OWL) as English sentences. This kind of question is more complex than those normally attempted by question generation systems, since mathematical word problems consist of a number of sentences that communicate a short narrative (in addition to providing the relevant numerical information required to solve the underlying mathematical problem). Thus, they embody many research issues that do not crop up with single-sentence questions. As well as describing the prototype system, I discuss five ways in which the difficulty of the generated questions may be controlled automatically during generation.

In this paper, we address the problem of communicating, interpreting,and executing complex yet abstract instructions to a robot teammember. This requires specifying the tasks in an unambiguous manner,translating them into operational procedures, and carrying outthose procedures in a persistent yet reactive manner. We reportour response to these issues, after which we demonstrate theircombined use in controlling a mobile robot in a multi-room officesetting on tasks similar to those in search-and-rescue operations.We conclude by discussing related research and suggesting directionsfor future work.

We discuss several improvements to the Question Generation Shared Task Evaluation Challenge (QGSTEC) system developed at the University of Pennsylvania in 2010. In addition to enhancing the question generation rules, we have implemented two new components to improve the ranking process. We use topic scoring, a technique developed for summarization, to identify important information for questioning, and language model probabilities to measure grammaticality. Preliminary experiments show that our approach is feasible.

Broadly speaking, information has something to do with order or organization within a system of elements. The thermodynamic concept of entropy is also associated with such systems, although in an inverse relationship. When we attempt to put these two apparently coordinated schemas of order and disorder together, all kinds of difficulties arise. I will briefly examine contemporary efforts to unify these two ways of conceiving order and show that they are substantially incompatible. In this process I will draw some distinctions that will lead to a broader reconciliation of the concepts of order and information. I will then attempt to re-evaluate the fundamental models behind these dissonant traditions for formulating order in an attempt to reframe a synthesis of conceptual structures that are mutually reconcilable. I will try to show that such a synthesis can finally make sense of the stubborn inconsistencies that persist in the ways Newtonian dynamics, thermodynamics and biology utilize the implicitly conflicting arrows of time.

We present an evaluation methodology and a system for ranking questions within the context of a multimodal tutorial dialogue. Such a framework has applications for automatic question selection and generation in intelligent tutoring systems. To create this ranking system we manually author candidate questions for specific points in a dialogue and have raters assign scores to these questions. To explore the role of question type in scoring, we annotate dialogue turns with labels from the DISCUSS dialogue move taxonomy. Questions are ranked using a SVM-regression model trained with features extracted from the dialogue context, the candidate question, and the human ratings. Evaluation shows that our system’s rankings correlate with human judgments in question ranking.

Information in bicycle pelotons consists of two main types: displayed information that is perceptible to others; and hidden information available to individual riders about their own physical state. Flow (or transfer) of information in pelotons occurs in two basic ways: 1) between cyclists within a peloton, which riders exploit to adjust tactical objectives (“intra-peloton”); 2) from sources outside a peloton as it is fed to riders via radio communication, or from third parties (“extra-peloton”). A conceptual framework is established for information transfer intra-peloton and extra-peloton. Both kinds of information transfer affect peloton complex dynamics. Pelotons exhibit mixed self-organized and top-down dynamics. These can be isolated and examined independently: self-organized dynamics emerge through local physical rules of interaction, and are distinguishable from the top-down dynamics of human competition, decision-making and information transfer. Both intra and extra-peloton information flow affect individual rider positions and the timing of their positional changes, but neither types of peloton information flow fundamentally alter self-organized structures. In addition to two previously identified peloton resources for which riders compete - energy saved by drafting, and near-front positions - information flow is identified as a third peloton resource. Also, building upon previous work on peloton phase-transitions and self-organized group-sorting, identified here is a transition between a team cluster state in which team-mates ride near each other, and a self-organized “fitness” cluster state in which riders of near equal fitness levels gravitate toward each other.

Next, through the Question Type Selection ArikIturri (Aldabe et al. 2006) is a system developed for the process, the question type is selected. Finally, by means automatic generation of different types of exercise. One of of the Question Construction step, the surface form of the the aims of ArikIturri is to generate items that could form question is created based on the previous steps. As regards part of real scenarios; this is why their creation is based our QG system, the sentence retriever module is responsible on topics that are part of the curriculum. Thus, the system for the Target Selection task and the item generator module is able to automatically generate tests from texts, to be included performs the Question Type Selection and Question Construction in testing tasks. The system is able to produce fill-inthe-blank processes.

The ability to delay intentions and remember them in the proper context is an important ability for general artificial agents. In this paper, we define the functional requirements of an agent capable of fulfilling delayed intentions with its long-term memories. We show that the long-term memories of different cognitive architec- tures share similar functional properties and that these mechanisms can be used to support delayed intentions. Finally, we do a preliminary evaluation of the different memories for fulfilling delayed intentions and show that there are trade-offs between memory types that warrant further research.

Computationally hard problems, like the Traveling Salesperson Problem, can be solved remarkably well by humans. Results obtained by computers are usually closer to the optimum, but require high computational effort and often differ from the human solutions. This paper introduces Greedy Expert Search (GES) that strives to show the same flexibility and efficiency of human solutions, while producing results of similarly high quality. The Traveling Salesperson Problem serves as an example problem to illustrate and evaluate the approach.

Our approach to mind-design derives from the view of language as a mirror of mind — a view compatible with the linguistic orientation of the Turing Test, and more concretely, with the remarkably tight coupling between linguistic structure and semantic entailment demonstrated by Richard Montague. Additional evidence for the power of this perspective comes from recent work in Natural Logic (NLog), in a sense a method of "reading off" certain obvious inferences directly from linguistic structure. Thus much of our past emphasis has been on developing a knowledge representation, Episodic Logic (EL), matching the expressivity of language, and inference machinery for this representation. More recently we have been striving to create broad bases of general world knowledge and lexical knowledge, while also adapting the latest version of our EPILOG inference engine to the kinds of obvious inferences that are the forte of NLog. At this point our knowledge collections range from sets of a few dozen core lexical axioms to millions of general "factoids" and quantified axioms derived from many of these, all expressed in EL. At the same time we have shown that EPILOG easily handles NLog-like inferences as well as ones beyond the scope of NLog.