Lehnert's Plot Units (Lehnert 1981) or Rumelhart's Story Grammars (Rumelhart 1980), and naturally, one would like We give semiformal We aim at capturing the informal human notion of equivalence definitions in § 2 and then give a few examples (without any of stories in a formal system in such a way that formal details) in § 3. two stories are perceived as equivalent when their formal representations are isomorphic (cf. There is no unique "human Comparing the adequacy of frameworks is not a formal task, notion of equivalence of stories" as the research on analogical but deals with the degree of representation of the informal reasoning shows (Rattermann and Gentner 1987; notions in the formal setting.

Traditional dialogue management systems are tightly coupled with the sensing ability of a single computer. How to organize an interaction in pervasive environments to provide a friendly and integrated interface to users is an important issue. This requires a transition of the human-computer interaction (HCI) from tight coupling to loose coupling. This paper proposes a hierarchical multimodal framework for pervasive interactions. Our system is designed to remind the activities of daily living for individuals with cognitive impairments.The system is composed of Markov decision processes for activity planing, and multimodal partially observable Markov decision processes for action planning and executing. Empirical results demonstrate the hierarchical multimodal framework establishes a flexible mechanism for pervasive interaction systems.

Processing the language of a narrative text, be it a novel, a extended flashbacks). These subsequent levels of blending film, or a play, is a crucial component of narrative of narrative spaces eventually yield the emergent space, comprehension. The research reported here shows how traditionally described as'the story'. The final product of processes driven by general linguistic and conceptual narrative comprehension is thus a mental construct, a patterns of meaning construction prompt the reader's or mega-blend, which emerges through multiple levels of viewer's response to the narrative artifact.

This paper summarizes the online machine learning method LCore, which enables robots to learn to communicate with users from scratch through verbal and behavioral interaction in the physical world. LCore combines speech, visual, and tactile information obtained through the interaction, and enables robots to learn beliefs regarding speech units, words, the concepts of objects, motions, grammar, and pragmatic and communicative capabilities. The overall belief system is represented by a dynamic graphical model in an integrated way. Experimental results show that through a small, practical number of learning episodes with a user, the robot was eventually able to understand even fragmental and ambiguous utterances, respond to them with confirmation questions and/or actions, generate directive utterances, and answer questions, appropriately for the given situation. This paper discusses the importance of a developmental approach to realize personally and physically situated human-robot conversations.

Developing the capabilities of children to comprehend written texts is key to their development as young adults. Text comprehension skills develop enormously from the age of 7- 8 until the age of 11. Nowadays, several young children (˜5% – 10% of novice readers) turn out to be poor (text) comprehenders: they demonstrate text comprehension difficulties, related to inference-making skills, despite proficiency in lowlevel cognitive skills like word decoding. Though there are several pencil-and-paper reading interventions for improving inference-making skills on text, and addressed to poor comprehenders, the design and evaluation of Adaptive Learning Systems (ALSs) are lagging behind. The use of more intelligent ALSs to custom-tailor such interventions in the form of games for poor comprehenders has tremendous potential. Our system embodies that potential. This paper presents the design of our ALS by focusing on its intelligent adaptive engine and the related conceptual models, and by presenting the visual interfaces for story telling and gaming.

In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic.In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the variation in the population across all complexities. Based on previous cultural algorithm approaches, we were to extend the existing models to produce a more general one that could be applied across all complexity classes. We produced a new version of the Cultural Algorithms Toolkit, CAT 2.0, which supported a variety of co-evolutionary features at both the Knowledge and Population levels. We then applied the system to the solution of a 150 randomly generated problems that ranged from simple to chaotic complexity classes. As a result we were able to produce the following conclusions: No homogeneous Social Fabric tested was dominant over all categories of complexity. As the complexity of problems increased, so did the complexity of the Social Fabric that was need to deal with it efficiently. In other words, there was experimental evidence that social structure can be related to the frequency and complexity type of the problems that presented to a cultural system.

Not all questions are asked with the same intention. Humans tend to address the implicit meaning of the question (that contributes to its pragmatic force), which requires knowledge of the context and a degree of common ground, more so than addressing the explicit propositional content of the question. Is recognizing the pragmatic force in today's human-robot dialogue systems worth the trouble? We focus on display questions (questions to which the asker already knows the answer) and argue that there are realistic human-robot interaction scenarios in existence today that would benefit from the deeper intention recognition. We also propose a method for obtaining display question annotations by embedding an elicitation question into the dialogue. The preliminary study of our robot receptionist shows that at least 16.7% of interactions with the embedded elicitation question include a display question.

The coagulation system (CS) is a complex, inter-connected biological system with major physiological and pathological roles. Adaptive mechanisms such as ubiquitous feedback and feedforward loops create non-linear relationships among its individual components and render the study of this biology at a molecular and cellular level nearly impossible. Computational modeling aims to overcome limitations of current analytical methods through in silico simulation of these complex interplays. We present herein an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. Our ABMS approach utilizes a subset of 48 rules to define the interactions among 24 enzymes and factors of the CS. These rules simulate the interaction of each “agent”, such as substrates, enzymes, and cofactors, on a two-dimensional grid of ~3,000 cells and ~500,000 agents. Our ABMS method demonstrates the robustness of the physiologic CS system over large ranges of tissue factor (TF) concentrations. The system also demonstrates fragility as complete coagulation occurs at sufficiently high concentrations of TF. Removal of individual coagulation inhibitors from the physiologic system results in system fragility at relatively lower TF concentrations. The complete removal of coagulation inhibitors leads to a system that is incapable of controlling coagulation at all TF concentrations. The synergistic effects of the inhibitory pathways create an intricate regulatory mechanism that allows sufficient clot formation while preventing system wide activation of the CS; a robust system emerges.

This paper addresses the numerical estimation of the principal curvature of pattern transformation manifolds. When a visual pattern undergoes a geometric transformation, it forms a (sub)manifold in the ambient space, which is usually called the transformation manifold. The manifold curvature is an important property characterizing the manifold geometry, with several applications in manifold learning. We propose an efficient numerical algorithm for estimating the principal curvature at a certain point on the transformation manifold.

This paper describes results from a large-scale, complex human study using non-facial and non-verbal affect for victim management in robot-assisted Urban Search and Rescue Applications. Statistically significant results are presented that indicate participants felt emotive robots were more calming, friendlier, and attentive.