The variability of human behavior during plan execution poses a difficult challenge for human-robot teams. In this paper, we use the concepts of theory of mind to enable robots to account for two sources of human variability during team operation. When faced with an unexpected action by a human teammate, a robot uses a simulation analysis of different hypothetical cognitive models of the human to identify the most likely cause for the human's behavior. This allows the cognitive robot to account for variances due to both different knowledge and beliefs about the world, as well as different possible paths the human could take with a given set of knowledge and beliefs. An experiment showed that cognitive robots equipped with this functionality are viewed as both more natural and intelligent teammates, compared to both robots who either say nothing when presented with human variability, and robots who simply point out any discrepancies between the human's expected, and actual, behavior. Overall, this analysis leads to an effective, general approach for determining what thought process is leading to a human's actions.

See Leuski et al. (2006) and to the same question -- for example, "What Leuski and Traum (2008) for more details. is your name?" -- depending on who the interactor The final parameter is the classification threshold is looking at. NPCEditor's user interface allows the on the KL-divergence value: only answers that designer to define arbitrary annotation classes or score above the threshold value are returned from categories and specify which of these annotation the classifier. The threshold is determined by tuning categories should be used in classification.

However, word concreteness is not an attribute that a A single word in the human language has many complex computer can directly compute. One means of assessing dimensions such as semantics, parts of speech, lexical type, the characteristics of words is by having humans rate them imagability, concreteness, familiarity, etc. It is important to on the dimensions of interest. Humans are proficient in know the dimensions of words in languages so that we can categorizing words into linguistic dimensions, but it is develop a better theoretical understanding of language and impractical to have humans rating tens of thousands of also to build tools that simulate human intelligence and words that we would need for psycholinguistic research.

This paper presents a discussion about assistive and augmentative natural language processing designed for certain disabled persons unable to communicate. Several approaches have been proposed, according to abilities of the writer. Here we distinguish two cases in the writer’s capacities: the writer knows alphabetic writing, or (s)he does not know it. In the first case, the idea is to assist the writer by completing the words or the group of words which are initially written. In the second case, pictograms are used instead of characters, but it must be decided if these pictograms represent concepts or words in a new writing system. If the pictograms represent concepts, the produced text may not correspond exactly to the wishes of the writer; whereas when the pictograms represent words, the writer has to change his (her) mental approach to write the words that (s)he has chosen in another way.

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses a statistical language classification technology for mapping from user's text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

Modeling human behavior requires vast quantities of accurately labeled training data, but for ubiquitous people-aware applications such data is rarely attainable. Even researchers make mistakes when labeling data, and consistent, reliable labels from low-commitment users are rare. In particular, users may give identical labels to activities with characteristically different signatures (e.g., labeling eating at home or at a restaurant as "dinner") or may give different labels to the same context (e.g., "work" vs. "office"). In this scenario, labels are unreliable but nonetheless contain valuable information for classification. To facilitate learning in such unconstrained labeling scenarios, we propose Community-Guided Learning (CGL), a framework that allows existing classifiers to learn robustly from unreliably-labeled user-submitted data. CGL exploits the underlying structure in the data and the unconstrained labels to intelligently group crowd-sourced data. We demonstrate how to use similarity measures to determine when and how to split and merge contributions from different labeled categories and present experimental results that demonstrate the effectiveness of our framework.

EMPATHICA is a computer program under development to facilitate cognitive-affective mapping using visual representations. A cognitive-affective map is a concept graph that includes information about the positive and negative emotional values of what is represented. Potential applications include conflict resolution, literary analysis, cross-cultural understanding, ethical assessment, authoring systems, and cognitive modeling.

Over the past decade, we have been engaged in an extensive research effort to build virtual humans and applications that use them. Building a virtual human might be considered the quintessential AI problem, because it brings together many of the key features, such as autonomy, natural communication, sophisticated reasoning and behavior, that distinguish AI systems. This paper describes major virtual human systems we have built and important lessons we have learned along the way.

Over the past decade, we have been engaged in an extensive research effort to build virtual humans and applications that use them.  Building a virtual human might be considered the quintessential AI problem, because it brings together many of the key features, such as autonomy, natural communication, sophisticated reasoning and behavior, that distinguish AI systems.  This paper describes major virtual human systems we have built and important lessons we have learned along the way.

Representing and comparing two-dimensional shapes is an important problem. Our hypothesis about human representations is that that people utilize two representations of shape: an abstract, qualitative representation of the spatial relations between the shape’s parts, and a detailed, quantitative representation. The advantage of relational, qualitative representations is that they facilitate shape comparison: two shapes can be compared via structural alignment processes which have been used to model similarity and analogy more broadly. This comparison process plays an important role in determining when two objects share the same shape, or in identifying transformations (rotations and reflections) between two shapes. Based on our hypothesis, we have built a computational model which automatically constructs both qualitative and quantitative representations and uses them to compare two-dimensional shapes in visual scenes. We demonstrate the effectiveness of our model by summarizing a series of studies which have simulated human spatial reasoning.