In this paper, we address the problem of computing the policy of an autonomous agent, taking human recommendations into account which could be appropriate for mixed initiative, or adjustable autonomy. For this purpose, we present Goal Biased Markov Decision Process (GBMDP) which assume two kinds of recommendation. The human recommends to the agent to avoid some situations (represented by undesirable states), or he recommends favorable situations represented by desirable states. The agent takes those recommendations into account by updating its policy (only updating the states concerned by the recommendations, not the whole policy). We show that GBMDP is efficient and it improves the human's intervention by reducing its time of attention paid to the agent. Moreover, GBMDP optimizes robot's computation time by updating only the necessary states. We also show how GBMDP can consider more than one recommendation. Finally, our experiments show how we update policies which are intractable by standard approaches.

Question ambiguity is one major factor that affects question quality. Less ambiguous questions can be produced by using more specific question words. We attack the problem of how to ask more specific questions by supplementing question words with the hypernyms for answer phrases. This dramatically increases the coverage of generated "which" questions. Evaluation results show improved question quality when the question words are disambiguated correctly given the context.

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.

Recent studies on question generation identify the need for automatic discourse analysers. We evaluated the feasibility of integrating an available discourse analyser called HILDA for a specific question generation system called CODA; introduce an approach by extracting a discourse corpus from the CODA parallel corpus; and identified future work towards automatic discourse analysis in the domain of question generation.

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.

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.

Organizations play a pivotal role in the dynamics of social, economic, and ecological systems. Current organizational life-cycle models do not adequately consider the impact of propensities (deeply ingrained preferences and patterns of behavior) on organizational culture and evolution. On a global basis, the predominant thinking modes in organizations are driven by senior executives, marketers, financial experts, legal resources, and the engineers and scientists that create our technology-rich world. Each of these groups has, in aggregate, embedded propensities or tendencies that profoundly shape decision-making patterns and overall social dynamics. Dominant propensities can make organizations vulnerable to risks by inhibiting the level of systems thinking and networking necessary to ensure integration within a global socio-ecological context. The spectrum of propensities within an organization shapes the relative resilience of its human and management systems, and ultimately determines organizational effectiveness. This paper proposes a model for organizational evolution that links the role of propensities to adaptability and resilience. Conscious effort to expand the intelligence of organizations through diversification of propensities better equips organizations to achieve adaptability and sustainability.

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.

This paper explores the application of computer simulation techniques to the fields of literary studies and narratology by developing a model for plot structure and characterization. Using a corpus of 19th Century British novels as a case study, the author begins with a descriptive quantitative analysis of character names, developing a set of stylized facts about the way narratives allocate attention to their characters. The author shows that narrative attention in many novels appears to follow a “long tail” distribution.The author then constructs an explanatory model in NetLogo, demonstrating that basic assumptions about plot structure are sufficient to generate output consistent with the real novels in the corpus.

Ant colony optimization (ACO) algorithms are computational problem-solving methods that are inspired by the complex behaviors of ant colonies; specifically, the ways in which ants interact with each other and their environment to optimize the overall performance of the ant colony. Our eventual goal is to develop and experiment with ACO methods that can more effectively adapt to dynamically changing environments and problems. We describe biological ant systems and the dynamics of their environments and behaviors. We then introduce a family of dynamic ACO algorithms that can handle dynamic modifications of their inputs. We report empirical results, showing that dynamic ACO algorithms can effectively adapt to time-varying environments.