Results 1  10
of
53
Reaching agreements through argumentation: a logical model and implementation
 ARTIFICIAL INTELLIGENCE
, 1998
"... ..."
(Show Context)
Tractable Reasoning via Approximation
 Artificial Intelligence
, 1995
"... Problems in logic are wellknown to be hard to solve in the worst case. Two different strategies for dealing with this aspect are known from the literature: language restriction and theory approximation. In this paper we are concerned with the second strategy. Our main goal is to define a semantical ..."
Abstract

Cited by 118 (0 self)
 Add to MetaCart
(Show Context)
Problems in logic are wellknown to be hard to solve in the worst case. Two different strategies for dealing with this aspect are known from the literature: language restriction and theory approximation. In this paper we are concerned with the second strategy. Our main goal is to define a semantically wellfounded logic for approximate reasoning, which is justifiable from the intuitive point of view, and to provide fast algorithms for dealing with it even when using expressive languages. We also want our logic to be useful to perform approximate reasoning in different contexts. We define a method for the approximation of decision reasoning problems based on multivalued logics. Our work expands and generalizes in several directions ideas presented by other researchers. The major features of our technique are: 1) approximate answers give semantically clear information about the problem at hand; 2) approximate answers are easier to compute than answers to the original problem; 3) approxim...
Active Logics: A Unified Formal Approach to Episodic Reasoning
"... Artificial intelligence research falls roughly into two categories: formal and implementational. This division is not completely firm: there are implementational studies based on (formal or informal) theories (e.g., CYC, SOAR, OSCAR), and there are theories framed with an eye toward implementabili ..."
Abstract

Cited by 36 (2 self)
 Add to MetaCart
Artificial intelligence research falls roughly into two categories: formal and implementational. This division is not completely firm: there are implementational studies based on (formal or informal) theories (e.g., CYC, SOAR, OSCAR), and there are theories framed with an eye toward implementability (e.g., predicate circumscription). Nevertheless, formal /theoretical work tends to focus on very narrow problems (and even on very special cases of very narrow problems) while trying to get them "right" in a very strict sense, while implementational work tends to aim at fairly broad ranges of behavior but often at the expense of any kind of overall conceptually unifying framework that informs understanding. It is sometimes urged that this gap is intrinsic to the topic: intelligence is not a unitary thing for which there will be a unifying theory, but rather a "society" of subintelligences whose overall behavior cannot be reduced to useful characterizing and predictive principles.
Learning to Reason with a Restricted View
, 1998
"... The Learning to Reason framework combines the study of Learning and Reasoning into a single task. Within it, learning is done specifically for the purpose of reasoning with the learned knowledge. Computational considerations show that this is a useful paradigm; in some cases learning and reasoning p ..."
Abstract

Cited by 35 (15 self)
 Add to MetaCart
(Show Context)
The Learning to Reason framework combines the study of Learning and Reasoning into a single task. Within it, learning is done specifically for the purpose of reasoning with the learned knowledge. Computational considerations show that this is a useful paradigm; in some cases learning and reasoning problems that are intractable when studied separately become tractable when performed as a task of Learning to Reason. In this paper we study Learning to Reason problems where the interaction with the world supplies the learner only partial information in the form of partial assignments. Several natural interpretations of partial assignments are considered and learning and reasoning algorithms using these are developed. The results presented exhibit a tradeoff between learnability, the strength of the oracles used in the interface, and the range of reasoning queries the learner is guaranteed to answer correctly.
A Logic of Intentions and Beliefs
, 1993
"... Intentions are an important concept in Artificial Intelligence and Cognitive Science. We present a formal theory of intentions... ..."
Abstract

Cited by 33 (7 self)
 Add to MetaCart
Intentions are an important concept in Artificial Intelligence and Cognitive Science. We present a formal theory of intentions...
Interactive Unawareness Revisited
 IN THEORETICAL ASPECTS OF RATIONALITY AND KNOWLEDGE: PROC. TENTH CONFERENCE (TARK 2005), 78–91
, 2005
"... We analyze a model of interactive unawareness introduced by Heifetz, Meier and Schipper (HMS). We consider ..."
Abstract

Cited by 29 (4 self)
 Add to MetaCart
We analyze a model of interactive unawareness introduced by Heifetz, Meier and Schipper (HMS). We consider
Reasoning About Knowledge: A Survey
 Handbook of Logic in Artificial Intelligence and Logic Programming
, 1995
"... : In this survey, I attempt to identify and describe some of the common threads that tie together work in reasoning about knowledge in such diverse fields as philosophy, economics, linguistics, artificial intelligence, and theoretical computer science, with particular emphasis on work of the past fi ..."
Abstract

Cited by 28 (2 self)
 Add to MetaCart
: In this survey, I attempt to identify and describe some of the common threads that tie together work in reasoning about knowledge in such diverse fields as philosophy, economics, linguistics, artificial intelligence, and theoretical computer science, with particular emphasis on work of the past five years, particularly in computer science. This articule is essentially the same as one that appears in Handbook of of Logic in Artificial Intelligence and Logic Programming, Vol. 4, D. Gabbay, C. J. Hogger, and J. A. Robinson, eds., Oxford University Press, 1995, pp. 134. It is a revised and updated version of a paper entitled "Reasoning about knowledge: a survey circa 1991", which appears in the Encyclopedia of Computer Science and Technology, Vol. 27, Supplement 12 (ed. A. Kent and J. G. Williams), Marcel Dekker, 1993, pp. 275296. That article, in turn is a revision of an article entitled "Reasoning About Knowledge: An Overview" that appears in Theoretical Aspects of Reasoning Abou...
On the Complexity of Entailment in Propositional Multivalued Logics
, 1997
"... Multivalued logics have a long tradition in the philosophy and logic literature that originates from the work by / Lukaszewicz in the 20's. More recently, many AI researchers have been interested in this topic for both semantic and computational reasons. Multivalued logics have indeed been freq ..."
Abstract

Cited by 23 (0 self)
 Add to MetaCart
Multivalued logics have a long tradition in the philosophy and logic literature that originates from the work by / Lukaszewicz in the 20's. More recently, many AI researchers have been interested in this topic for both semantic and computational reasons. Multivalued logics have indeed been frequently used both for their semantic properties and as tools for designing tractable reasoning systems. We focus here on the computational aspects of multivalued logics. The main result of this paper is a detailed picture of the impact that the semantic definition, the syntactic form and the assumptions on the relative sizes of the inputs have on the complexity of entailment checking. In particular we show new polynomial cases and generalize polynomial cases already known in the literature for various popular multivalued logics. Such polynomial cases are obtained by means of two simple algorithms, sharing a common method. Dipartimento di Informatica e Sistemistica, Universit`a di Roma "La Sapien...
What is an Inference Rule?
 Journal of Symbolic Logic
, 1992
"... What is an inference rule? This question does not have a unique answer. One usually nds two distinct standard answers in the literature: validity inference ( ` v ' if for every substitution , the validity of [] entails the validity of [']), and truth inference ( ` t ' if for every ..."
Abstract

Cited by 21 (1 self)
 Add to MetaCart
(Show Context)
What is an inference rule? This question does not have a unique answer. One usually nds two distinct standard answers in the literature: validity inference ( ` v ' if for every substitution , the validity of [] entails the validity of [']), and truth inference ( ` t ' if for every substitution , the truth of [] entails the truth of [']). In this paper we introduce a general semantic framework that allows us to investigate the notion of inference more carefully. Validity inference and truth inference are in some sense the extremal points in our framework. We investigate the relationship between various types of inference in our general framework, and consider the complexity of deciding if an inference rule is sound, in the context of a number of logics of interest: classical propositional logic, a nonstandard propositional logic, various propositional modal logics, and rstorder logic.
Logical Omniscience as a Computational Complexity Problem
, 2009
"... The logical omniscience feature assumes that an epistemic agent knows all logical consequences of her assumptions. This paper offers a general theoretical framework that views logical omniscience as a computational complexity problem. We suggest the following approach: we assume that the knowledge o ..."
Abstract

Cited by 19 (7 self)
 Add to MetaCart
The logical omniscience feature assumes that an epistemic agent knows all logical consequences of her assumptions. This paper offers a general theoretical framework that views logical omniscience as a computational complexity problem. We suggest the following approach: we assume that the knowledge of an agent is represented by an epistemic logical system E; we call such an agent not logically omniscient if for any valid knowledge assertion A of type F is known, a proof of F in E can be found in polynomial time in the size of A. We show that agents represented by major modal logics of knowledge and belief are logically omniscient, whereas agents represented by justification logic systems are not logically omniscient with respect to t is a justification for F.