"An information system characterizes a view of the world with which it interacts, and broadly speaking, its input can take two forms; a query or an impetus for change. Physically the information held by an information system might be a diagram, a graph, a spreadsheet, a database, a rulebase, or a more sophisticated cognitive entity. More often than not, information is uncertain and subject to change; this is the case even for simple database systems. Consequently an information system requires a mechanism for modifying its view as more information about the world is acquired."
– Mary-Anne Williams. Tutorial: Belief Revision: Modeling The Dynamics Of Information Systems, 1995.
Conceptual Spaces--The Geometry of Thought is a book by Peter Gärdenfors, professor of cognitive science at Lund University, Sweden. Gärdenfors has authored another book in this series (based on work with Carlos Alchourron and David Makinson), Knowledge in Flux, a definitive account of the widely examined AGM (after Alchourron, Gärdenfors, and Makinson) theory of belief revision. The AGM theory is firmly based on classical logic and its model theory, and by his founding participation in developing it, Gärdenfors has earned the right to critique knowledge representation. His new book is not primarily about logic, but it is certainly not an apostasy either. If I may be permitted a minor irreverence, I would say that this book came not to destroy logic but to fulfill.
The idea is that although an AI system without the frame problem might, say, read an echocardiogram and diagnose a heart defect, a really smart autonomous robot will arrive only if, like us humans, it can handle the frame problem. The highlight … is an entertaining go-round between two pugilists trading blows in civil but gloves-off style, reminiscent of a net discussion. We're still confronted by a difficult question: Is there a solution to it? If not, then R2D2 might forever be but a creature of fiction. If, however, the frame problem is solvable, we must confront yet another question: Is there a general solution to the frame problem, or is the best that can be mustered a so-called domain-dependent solution?
Haussler's paper was therefore important in linking the new PAC learning theory work with the ongoing work on machine learning within AI. Twenty years later that link is firmly established, and the two research communities have largely merged into one. In fact, much of the dramatic progress in machine learning over the past two decades has come from a fruitful marriage between research on learning theory and design of practical learning algorithms for particular problem classes. 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 Mc-Dermott.
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ABSTRACT To choose their actions, reasoning programs must be able to make assumptions and subsequently revise their beliefs when discoveries contradict these assumptions. The Truth Maintenance System (TMs) is a problem solver subsystem for performing these functions by recording and maintaining the reasons for program beliefs. Such recorded reasons are useful in constructing explanations of program actions and in guiding the course of action of a problem solver. In memory of John Sheridan Mac Nerney 1. Introduction Computer reasoning programs usually construct computational models of situations. To keep these models consistent with new information and changes in the situations being modelled, the reasoning programs frequently need to remove or change portions of their models.
Example: Generating a Design. 1 2. The Basic Intuition: Assuming a Subgoal is Possible 1 3. Generation of a Digital Design 2 3.1. Example: Generating a Design Given a description of legitimate components of a circuit, say Nor-gates and Wire s, and the goal (as in r.xamplc 1.1) sum 11012 / In our half adder example, we start only with what we know about circuits. What else would have to be true in order to prove the goal? We will make a list of such items. If all the items on the list can simultaneously be accomplished, then we have a valid design.
Selecting extended logic programming with the answer-set semantics as a "generic" nonmonotonic logic, we show how that logic defines preferred belief sets and how preferred belief sets allow us to represent and interpret normative statements. Conflicts among program rules (more generally, defaults) give rise to alternative preferred belief sets. Finally, we comment on formalisms which explicitly represent preferences on properties of belief sets. Such formalisms either build preference information directly into rules and modify the semantics of the logic appropriately, or specify preferences on belief sets independently of the mechanism to define them.
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.
Truth maintenance is a collection of techniques for doing belief revision. A truth maintenance system's task is to maintain a set of beliefs in such a way that they are not known to be contradictory and no belief is kept without a reason. Truth maintenance systems were introduced in the late seventies by Jon Doyle and in the last five years there has been an explosion of interest in this kind of systems. In this paper we present an annotated bibliography to the literature of truth maintenance systems, grouping the works referenced according to several classifications.