Many people fail to properly evaluate Internet information. This is often due to a lack of understanding of the issues surrounding evaluation and authority, and, more specifically, a lack of understanding of the structure and modi operandi of the Internet and the Domain Name System. The fact that evaluation is not being properly performed on Internet information means both that questionable information is being used recklessly, without adequately assessing its authority, and good information is being disregarded, because trust in the information is lacking. Both scenarios may be resolved by ascribing proper amounts of cognitive authority to Internet information. Traditional measures of authority present in a print environment are lacking on the Internet, and, even when occasionally present, are of questionable veracity. A formal model and evaluative criteria are herein suggested and explained to provide a means for accurately ascribing cognitive authority in a networked environment; the model is unique in its representation of overt and covert affiliations as a mechanism for ascribing proper authority to Internet information.

We discuss the model-acquisition and model-matching aspects of a model-based vision system for the bin-picking of twisted tubular parts. The system uses both 3-D structured-light vision and 2-D binary vision in a synergistic manner. Binary vision kicks in when the robot picks up a tube using a graspable fragment identified as such by the 3-D vision system; the tube thus picked up is placed on a backlit table so that its pose can be calculated from its 2D image with sufficient precision to allow its assembly with another object. The binary vision carries out pose calculation by affine matching the projected image with a small set of model projections, each corresponding to a separate stable pose of the object on a flat surface and each represented by the coordinates of only four points. This paper presents a heuristic algorithm for determining the placement of the four points for representing the model projections.>

An architecture for reasoning with uncertainty about the identities of objects in a scene is described. The main components of this architecture create and assign credibility to object hypotheses based on feature-match, object, relational, and aspect consistencies. The Dempster-Shafer formalism is used for representing uncertainty, so these credibilities are expressed as belief functions which are combined using Dempster's combination rule to yield the system's aggregate belief in each object hypothesis. One of the principal objections to the use of Dempster's rule is that its worst-case time complexity is exponential in the size of the hypothesis set. The structure of the hypothesis sets developed by this system allow for a polynomial implementation of the combination rule. Experimental results affirm the effectiveness of the method in assessing the credibility of candidate object hypotheses.>

The authors present an approach to planning sensing strategies in a robot workcell with multisensor capabilities. The system first forms an initial set of object hypotheses by using one of the sensors. Subsequently, the system reasons over different possibilities for selecting the next sensing operation, this being done in a manner so as to maximally disambiguate the initial set of hypotheses. The 'next sensing operation' is characterized by both the choice of the sensor and the viewpoint to be used. Aspect graph representation of objects plays a central role in the selection of the viewpoint, these representations being derived automatically by a solid modelling program.>

World representation is obviously a most important part of the sensory intelligence required by a mobile robot in order to operate autonomously in a complex environment. In this paper, we will propose a taxonomy of robot intelligence based on the methods used for world representation and for the integration of sensory information with the representations. These methods of representation and sensory integration are currently being programmed into PETER, a mobile robot, in our lab.

A major hurdle in the development of intelligent robots is that we still do not possess efficient computational and representational methodologies for emulating knowledge and expectation driven behavior so basic to human cognition and problem solving. Even if we use techniques such as geometric modeling for representing objects in the robot world, we are still lacking in methods for linking such representations with sensory feedback. In this paper, we have proposed the use of intermediate representations - we call them sensor-tuned representations - for linking CSG based solid modeling with sensory information. We also discuss how sensor-tuned representations are constructed from range data and how object recognition can be done with sensor-tuned representations. Finally, we show results of manipulation experiments produced by the current implementation of the system.