Levesque, Hector


ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains

AAAI Conferences

High-level programming languages are an influential control paradigm for building agents that are purposeful in an incompletely known world. GOLOG, for example, allows us to write programs, with loops, whose constructs refer to an explicit world model axiomatized in the expressive language of the situation calculus. Over the years, GOLOG has been extended to deal with many other features, the claim being that these would be useful in robotic applications. Unfortunately, when robots are actually deployed, effectors and sensors are noisy, typically characterized over continuous probability distributions, none of which is supported in GOLOG, its dialects or its cousins. This paper presents ALLEGRO, a belief-based programming language for stochastic domains, that refashions GOLOG to allow for discrete and continuous initial uncertainty and noise. It is fully implemented and experiments demonstrate that ALLEGRO could be the basis for bridging high-level programming and probabilistic robotics technologies in a general way.


How to Progress Beliefs in Continuous Domains

AAAI Conferences

When Lin and Reiter introduced the progression of basic action theories in thesituation calculus, they were essentially motivated by long-lived roboticagents functioning over thousands of actions. However, their account does notdeal with probabilistic uncertainty about the initial situation nor witheffector or sensor noise, as often needed in robotic applications. In thispaper, we obtain results on how to progress continuous degrees of beliefagainst continuous effector and sensor noise in a semantically correctfashion. Most significantly, and perhaps surprisingly, we identify conditionsunder which our account is not only as efficient as the filtering mechanismscommonly used in robotics, but considerably more general.


Forgetting in Action

AAAI Conferences

In this paper we develop a general framework that allows for both knowledge acquisition and forgetting in the Situation Calculus. Based on the Scherl and Levesque (Scherl and Levesque 1993) possible worlds approach to knowledge in the Situation Calculus, we allow for both sensing as well as explicit forgetting actions. This model of forgetting is then compared to existing frameworks. In particular we show that forgetting is well-behaved with respect to the contraction operator of the well-known AGM theory of belief revision (Alchourron, Gardenfors, and Makinson 1985) but that knowledge forgetting is distinct from the more commonly known notion of logical forgetting (Lin and Reiter 1994).


The Winograd Schema Challenge

AAAI Conferences

In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. A Winograd schema is a pair of sentences that differ only in one or two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. We have compiled a collection of Winograd schemas, designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora. A contestant in the Winograd Schema Challenge is presented with a collection of one sentence from each pair, and required to achieve human-level accuracy in choosing the correct disambiguation.


Is It Enough to Get the Behaviour Right?

AAAI Conferences

This paper deals with the relationship between intelligent behaviour, on the   one hand, and the mental qualities needed to produce it, on the other.  We   consider two well-known opposing positions on this issue: one due to Alan   Turing and one due to John Searle (via the Chinese Room).  In particular, we   argue against Searle, showing that his answer to the so-called System Reply   does not work.  The argument takes a novel form:   we shift the debate to a different and more plausible room where the   required conversational behaviour is much easier to characterize and to   analyze.  Despite being much simpler than the Chinese Room, we show that    the  behaviour there is still complex enough that it cannot be produced without   appropriate mental qualities.


The 2005 AAAI Classic Paper Awards

AI Magazine

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.


The 2005 AAAI Classic Paper Awards

AI Magazine

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.


In Memory of Ray Reiter (1939-2002)

AI Magazine

Ray dedicated his life to his research with the wonder of a child, the fearlessness of an explorer, the precision of a mathematician, and the tirelessness of a researcher who found shallowness and confusion intolerable. He leaves a legacy of groundbreaking, deep insights that have changed the course of AI.


Knowledge representation and reasoning

Classics

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