Interval-Based Techniques for Sensor Data Fusion

We view the problem of sensor-based decision-making in terms of two components: a sensor fusion component that isolates a set of models consistent with observed data, and an evaluation component that uses this information and task-related information to make model-based decisions. This paper describes a procedure for computing the solution set of parametric equations describing a sensor-object imaging relationship. Given a parametric form with s parameters, we show that this procedure can be implemented using a parallel array of 6s2 processors. We then describe an application of these techniques which demonstrates the use of task-related information and set-based decision-making methods

Information Maps for Active Sensor Control

This paper outlines our current progress in active sensor control. We consider the problem of controlling a nonlinear observation system observing data implicitly related to parameters of interest. We show how linear estimation theory can be applied to this problem, and develop the notion of an information map showing the information expected from sensor viewpoints. We discuss the robustness of these techniques, and propose a method to enhance their robustness. We expect these maps to be useful in active sensor control

Deciding Not to Decide Using Resource-Bounded Sensing

We view the problem of sensor-based decision-making in terms of two components: a sensor fusion component that isolates a set of models consistent with observed data, and an evaluation component that uses this information and task-related information to make model-based decisions. In previous work we have described a procedure for computing the solution set of parametric equations describing a sensor-object imaging relationship, and also discussed the use of task-specific information to support set-based decision-making methods. In this paper, we investigate the implications of allowing one of the decision-making options to be no decision, whereupon a human might be called to aid or interact with the system. In particular, this type of capability supports the construction of supervised or partially autonomous systems. We discuss how such situations might arise and give concrete examples of how a system might reach such a decision using our techniques

Computational Aspects of Proofs in Modal Logic

Various modal logics seem well suited for developing models of knowledge, belief, time, change, causality, and other intensional concepts. Most such systems are related to the classical Lewis systems, and thereby have a substantial body of conventional proof theoretical results. However, most of the applied literature examines modal logics from a semantical point of view, rather than through proof theory. It appears arguments for validity are more clearly stated in terms of a semantical explanation, rather than a classical proof-theoretic one. We feel this is due to the inability of classical proof theories to adequately represent intensional aspects of modal semantics. This thesis develops proof theoretical methods which explicitly represent the underlying semantics of the modal formula in the proof. We initially develop a Gentzen style proof system which contains semantic information in the sequents. This system is, in turn, used to develop natural deduction proofs. Another semantic style proof representation, the modal expansion tree is developed. This structure can be used to derive either Gentzen style or Natural Deduction proofs. We then explore ways of automatically generating MET proofs, and prove sound and complete heuristics for that procedure. These results can be extended to most propositional system using a Kripke style semantics and a fist order theory of the possible worlds relation. Examples are presented for standard T, S4, and S5 systems, systems of knowledge and belief, and common knowledge. A computer program which implements the theory is briefly examined in the appendix

Real-Time Vision-Based Robot Localization

In this article we describe an algorithm for robot localization using visual landmarks. This algorithm determines both the correspondence between observed landmarks (in this case vertical edges in the environment) and a pre-loaded map, and the location of the robot from those correspondences. The primary advantages of this algorithm are its use of a single geometric tolerance to describe observation error, its ability to recognize ambiguous sets of correspondences, its ability to compute bounds on the error in localization, and fast performance. The current version of the algorithm has been implemented and tested on a mobile robot system. In several hundred trials the algorithm has never failed, and computes location accurate to within a centimeter in less than half a second

Explaining Modal Logic Proofs

There has recently been considerable progress in the area of using computers as a tool for theorem proving. In this paper we focus on one facet of human-computer interaction in such systems: generating natural language explanations from proofs. We first discuss the X proof system - a tactic style theorem proving system for first-order logic with a collection of inference rules corresponding to human-oriented proof techniques. In X, proofs are stored as they are discovered using a structured term representation. We describe a method for producing natural language explanations of proofs via a simple mapping algorithm from proof structures to text. Nonclassical or specialized logics are often used in specialized applications. For example, modal logics are often used to reason about time and knowledge, and inheritance theories are often developed for classification systems. The form of, and explanations for, proofs in these systems should be tailored to reflect their special features. In this paper, we focus on the extension of X to incorporate proofs in modal logic, and on the different kinds of explanations of modal proofs that can be produced to meet the needs of different users

Computational Methods for Task-Directed Sensor Data Fusion and Sensor Planning

In this paper, we consider the problem of task-directed information gathering. We first develop a decision-theoretic model of task-directed sensing in which sensors are modeled as noise-contaminated, uncertain measurement systems and sensing tasks are modeled by a transformation describing the type of information required by the task, a utility function describing sensitivity to error, and a cost function describing time or resource constraints on the system. This description allows us to develop a standard conditional Bayes decision-making model where the value of information, or payoff, of an estimate is defined as the average utility (the expected value of some function of decision or estimation error) relative to the current probability distribution and the best estimate is that which maximizes payoff. The optimal sensor viewing strategy is that which maximizes the net payoff (decision value minus observation costs) of the final estimate. The advantage of this solution is generality--it does not assume a particular sensing modality or sensing task. However, solutions to this updating problem do not exist in closed-form. This, motivates the development of an approximation to the optimal solution based on a grid-based implementation of Bayes\u27 theorem. We describe this algorithm, analyze its error properties, and indicate how it can be made robust to errors in the description of sensors and discrepancies between geometric models and sensed objects. We also present the results of this fusion technique applied to several different information gathering tasks in simulated situations and in a distributed sensing system we have constructed