Only knowing captures the intuitive notion that the beliefs of an agent are precisely those that follow from its knowledge base. It has previously been shown to be useful in characterizing knowledge-based reasoners, especially in a quantified setting. While this allows us to reason about incomplete knowledge in the sense of not knowing whether a formula is true or not, there are many applications where one would like to reason about the degree of belief in a formula. In this work, we propose a new general first-order account of probability and only knowing that admits knowledge bases with incomplete and probabilistic specifications. Beliefs and non-beliefs are then shown to emerge as a direct logical consequence of the sentences of the knowledge base at a corresponding level of specificity.

The area of cognitive robotics is often subject to the criticism that the proposals investigated in the literature are too far removed from the kind of continuous uncertainty and noise seen in actual real-world robotics. This paper proposes a new language and an implemented system, called PREGO, based on the situation calculus, that is able to reason effectively about degrees of belief against noisy sensors and effectors in continuous domains. It embodies the representational richness of conventional logic-based action languages, such as context-sensitive successor state axioms, but is still shown to be efficient using a number of empirical evaluations. We believe that PREGO is a powerful framework for exploring real-time reactivity and an interesting bridge between logic and probability for cognitive robotics applications.

Among the many approaches for reasoning about degrees of belief inthe presence of noisy sensing and acting, the logical accountproposed by Bacchus, Halpern, and Levesque is perhaps the most expressive.While their formalism is quite general, it is restricted to fluentswhose values are drawn from discrete countable domains, as opposed tothe continuous domains seen in many robotic applications. In thispaper, we show how this limitation in their approach can be lifted.By dealing seamlessly with both discrete distributions and continuousdensities within a rich theory of action, we provide a very generallogical specification of how belief should change after acting andsensing in complex noisy domains.

Mitchell and Levesque provide commentary on the two AAAI Classic Paper awards, given at the AAAI-05 conference in Pittsburgh, Pennsylvania. The two winning papers were "Quantifying the Inductive Bias in Concept Learning," by David Haussler, and "Default Reasoning, Nonmonotonic Logics, and the Frame Problem," by Steve Hanks and Drew McDermott.

Twenty years later that link is firmly established, and the two research communities have largely merged into one. Problem," by Steve Hanks and Drew Mc-or does not hold after a sequence of learning was the "inductive bias" of a The idea is this: Normally, an object learn the target concept--the more in 1986, helped initiate a very constraining the inductive bias, the is unaffected by an action. If a window fruitful integration of a branch of machine less training data needed. Starting in the of PAC learning was being developed, an action. There are clear exceptions, 1950s, with work like Samuels's program which allowed deriving quantitative however, such as the act of closing the that learned strategies for playing bounds on the probability of window. A variety of formal systems checkers, AI researchers had designed successful learning as a function of the have been proposed that would allow and experimented with a number of training examples and the us to infer in the absence of conflicting variety of learning algorithms and complexity of the learner's hypothesis information that the window remains had also developed a number of theoretical space (as measured by its Vapnik-open (or that a polar bear is results, such as convergence Chervonenkis dimension). What white or that a violin has four strings, proofs for perceptrons and "learning Haussler's paper did was help introduce and so on).

He leaves a legacy of groundbreaking, deep insights that have changed the course of AI. "Only one same reason is shared by all of us: we wish to create worlds as real as, but other than the world that is." The quotation captures what was special about Ray: He had an adventurer's desire to go beyond the boundaries of our current understanding, together with a mathematician's insistence on precision. Ray the adventurer was always eager to try new ideas and directions. He was not afraid to enter murky areas, and he always left them better illuminated. He introduced terms to the AI community such as default logic, closed-world assumption, and cognitive robotics; he opened avenues of theoretical research with new resolution proof methods and logics for nonmonotonic reasoning, diagnosis, and action; and he was the prime mover in the Cognitive Robotics initiative that has led to a whole new program of research.

See also:A Fundamental Tradeoff in Knowledge Representation and Reasoning. Slides. Department of Computer and Information Science. Norwegian University of Science and Technology. IT3706 - Knowledge Representation and Modelling, 2005.Knowledge Representation and Reasoning. Morgan Kaufmann, 2004.Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1989.Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (1st ed.). James Allen, Ronald J. Brachman, Erik Sandewall, Hector J. Levesque, Ray Reiter, and Richard Fikes (Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.Annual Review of Computer Science Vol. 1: 255-287

Proc. Fourth National Conference on Artificial Intelligence (AAAI-84), Austin, Texas.