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This paper presents algorithms fi)r computing constraints on the pusiliun of' an object due o the presence of obstacles. This problem arises in applicalion'.g xYhMi require choosing how to arrange or move obje(:[s among other objects. The basis uf the approach presenlcd here is to characterize Ihe positinn and orientation of the-object of interest as a single point in a Coufiguration Space, in which each coordinate represents a degree of fi'eedom in the position. nnd/o' orientation of the object. The configurations forbidden to this object, due to the presence of obstacles, can then be characterized as regions in the Configuration Space. The paper presents algorithms for computing these Configuration Space obstacles when the objects and obstacles are polygons or polyhedra. An approximation technique fi.>r high-dilnensional Configuration Space obstacles, based ou projections of obstacles slices, is described

In order to build autonomous robots that can carry out useful work in unstructured environments new approaches have been developed to building intelligent systems. The relationship to traditional academic robotics and traditional artificial intelligence is examined. In the new approaches a tight coupling of sensing to action produces architectures for intelligence that are networks of simple computational elements which are quite broad, but not very deep. Recent work within this approach has demonstrated the use of representations, expectations, plans, goals, and learning, but without resorting to the traditional uses, of central, abstractly manipulable or symbolic representations. Perception within these systems is often an active process, and the dynamics

This paper presents an implemented system for recognizing the occurrence of events described by simple spatial-motion verbs in short image sequences. The semantics of these verbs is specified with event-logic expressions that describe changes in the state of force-dynamic relations between the participants of the event. An efficient finite representation is introduced for the infinite sets of intervals that occur when describing liquid and semi-liquid events. Additionally, an efficient procedure using this representation is presented for inferring occurrences of compound events, described with event-logic expressions, from occurrences of primitive events. Using force dynamics and event logic to specify the lexical semantics of events allows the system to be more robust than prior systems based on motion profile. 1.

In this paper, we show that in the best-known version of the blocks world (and several related versions), planning is difficult, in the sense that finding an optimal plan is NP-hard. However, the NP-hardness is not due to deleted-condition interactions, but instead due to a situation which we call a deadlock. For problems that do not contain deadlocks, there is a simple hill-climbing strategy that can easily find an optimal plan, regardless of whether or not the problem contains any deleted-condition interactions. The above result is rather surprising, since one of the primary roles of the blocks world in the planning literature has been to provide examples of deleted-condition interactions such as creative destruction and Sussman's anomaly. However, we can explain why deadlocks are hard to handle in terms of a domain-independent goal interaction which we call an enabling-condition interaction, in which an action invoked to achieve one goal has a side-effect of making it easier to achi...

Abstract not found

Statistical language learning research takes the view that many traditional natural language processing tasks can be solved by training probabilistic models of language on a sufficient volume of training data. The design of statistical language learners therefore involves answering two questions: (i) Which of the multitude of possible language models will most accurately reflect the properties necessary to a given task? (ii) What will constitute a sufficient volume of training data? Regarding the first question, though a variety of successful models have been discovered, the space of possible designs remains largely unexplored. Regarding the second, exploration of the design space has so far proceeded without an adequate answer. The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: it identifies a new class of designs by providing a novel theory of statistical natural language processing, and it presents the foundations for a predictive theory of data requirements to assist in future design explorations. The first of these contributions is called the meaning distributions theory. This theory

this paper a new approach, based on a theory of the process of focusing on parts of the discourse, is used to explain the interpretation of anaphors. The concept of a speaker's foci is defined, and their use is demonstrated in choosing the interpretations of personal pronouns. The rules for choosing interpretations are stated within a framework that shows: how to control search in inferring by a new method called constraint checking; how to take advantage of syntactic, semantic and discourse constraints on interpretation; and how to generalize the treatment of personal pronouns, to serve as a framework for the theory of interpretation for all anaphors

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This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible...