To integrate robotics into society, it is first necessary to measure and analyze current societal responses to areas within robotics. This article is the second in a continuing series of reports on the societal effects of various aspects of robotics. In my previous article, I discussed the problems of sensor abuse and outlined a program of treatment. However, despite the wide dissemination of that article, there are still numerous empty beds at the Susan Calvin Clinic for the Prevention of Sensor Abuse. Sensor abuse continues unabated despite strong evidence that there is a better way. In this article, I explore the age-old question, Why does the robotics community look down on efficient sensing systems?

The information in this article was originally presented as a keynote invited talk by Susan Lederman at the Thirteenth International Joint Conference on Artificial Intelligence in Chambery, France; it is based primarily on a joint research program that we conducted. We explain how the scientific study of biological systems offers a complementary approach to the more formal analytic methods favored by roboticists; such study is also relevant to a number of classical problems addressed by the AI field. We offer an example of the scientific approach that is based on a selection of our experiments and empirically driven theoretical work on human haptic (tactual) object processing; the nature and role of active manual exploration is of particular concern. We further suggest how this program with humans can be modified and extended to guide the development of highlevel manual exploration strategies for robots equipped with a haptic perceptual system.

He knew the challenges opinion, as one of the premier natural language were great and would require the research groups in the world. He gave efforts of many people. He had a genius for one of us (Barbara Grosz) her first AI job, even bringing these people together. In doing so, he took a of people who had known Don over the risk of a magnitude that she fully appreciated years to send us reminiscences. Although only years later when she herself was hiring each person's story differed, a striking commonality research associates.

The competition, rules, coordinating the setup and Technologies, showed off a unique which attracted teams from administration of the contest, and global-positioning system using a many of the top mobile robotics trying to cope with the needs of the robot-mounted revolving laser and research laboratories in the United 15 teams that put so much energy three or more stationary receivers. States (see side bar), was first proposed into their entries. This article reports Still, many teams suffered frustrating by Thomas Dean and held at some of the experiences I had in failures in hardware and especially the 1992 NCAI conference. Dean's helping to design and run the contest software, leading to a general lack concept was to further the research and some reflections, drawn of sleep and noticeable exhaustion into the skills such robots from post mortem abstracts written among the contestants by Monday need--sensing, interpretation, planning, by the competitors, on the relation of night, the day before the contest. I and reacting--by bringing the contest to current research efforts know this from personal experience: together interested parties in a cooperative in mobile robotics.

The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the ``flow'' of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair flawed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories.

Certain species of freshwater tropical fish, known as weakly electric fish, possess an active electric sense that allows them to detect and discriminate objects in their environment using a self-generated electric field (Bullock and Heiligenberg, 1986). They detect objects by sensing small perturbations in this electric field using an array of specialized receptors, known as electroreceptors, that cover their body surface. Weaklyelectric fish often live in turbid water and tend to be nocturnal. These conditions, which hinder visual perception, do not adversely affect the electric sense. Hence the electrosensory system allows these fish to navigate and capture prey in total darkness in much the same way as the sonar system of echolocating bats allows them to do the same.

A switching between apparently coherent (oscillatory) and stochastic episodes of activity has been observed in responses from cat and monkey visual cortex. We describe the dynamics of these phenomena in two parallel approaches,a phenomenological and a rather microscopic one. On the one hand we analyze neuronal responses in terms of a hidden state model (HSM). The parameters of this model are extracted directly from experimental spiketrains. They characterize the underlying dynamics as well as the coupling of individual neurons to the network. This phenomenological modelthus provides a new framework for the experimental analysis of network dynamics.

This is motivated by experimental evidencethat these phenomena may be subserved by the same mechanisms. An important aspect of this model is that ocular dominance segregationcan occur when input activity is both distributed, and positively correlated between the eyes. This allows investigation of the dependence of the pattern of ocular dominance stripes on the degree of correlation between the eyes: it is found that increasing correlation leads to narrower stripes. Experiments are suggested to test whether such behaviour occursin the natural system.

A boosting algorithm converts a learning machine with error rate less than 50% to one with an arbitrarily low error rate. However, the algorithm discussed here depends on having a large supply of independent training samples. We show how to circumvent this problem and generate an ensemble of learning machines whose performance in optical character recognition problems is dramatically improved over that of a single network. We report the effect of boosting on four databases (all handwritten) consisting of 12,000 digits from segmented ZIP codes from the United State Postal Service (USPS) and the following from the National Institute of Standards and Testing (NIST): 220,000 digits, 45,000 upper case alphas, and 45,000 lower case alphas. We use two performance measures: the raw error rate (no rejects) and the reject rate required to achieve a 1% error rate on the patterns not rejected.

Certain species of freshwater tropical fish, known as weakly electric fish, possess an active electric sense that allows them to detect and discriminate objects in their environment using a self-generated electric field (Bullock and Heiligenberg, 1986). They detect objects by sensing small perturbations in this electric field using an array of specialized receptors, known as electroreceptors, that cover their body surface. Weakly electric fish often live in turbid water and tend to be nocturnal. These conditions, which hinder visual perception, do not adversely affect the electric sense. Hence the electrosensory system allows these fish to navigate and capture prey in total darkness in much the same way as the sonar system of echolocating bats allows them to do the same.