Non-standard theories of uncertainty in knowledge representation and reasoning

1994 ◽  
Vol 9 (4) ◽  
pp. 399-416 ◽  
Author(s):  
Didier Duboid ◽  
Henri Prade
Keyword(s):  
State Of The Art ◽  
Nonmonotonic Reasoning ◽  
Possibility Theory ◽  
Lower Probability ◽  
Knowledge Representation And Reasoning ◽  
Uncertainty Modelling ◽  
Factual Information ◽  
Inference Mechanism ◽  
Correct Representation ◽  
Nonmonotonic Inference

AbstractThis paper provides a survey of the state of the art in plausible reasoning, that is exception tolerant reasoning under incomplete information. Three requirements are necessary for a formalism in order to cope with this problem: (i) making a clear distinction between factual information and generic knowledge; (ii) having a correct representation of partial ignorance; (iii) providing a nonmonotonic inference mechanism. Classical logic fails on requirements (i) and (iii), whilst the Bayesian approach does not fulfil (ii) in an unbiased way. In this perspective, various uncertainty modelling frameworks are reviewed: MYCIN-like fully compositional calculi, belief functions, upper and lower probability systems, and possibility theory. Possibility theory enables classical logic to be extended to layered sets of formulae, where layers express certainty levels. Finally, it is explained how generic knowledge can be expressed by constraints on possibility measures, and how possibilistic inferences can encode nonmonotonic reasoning in agreement with the Lehmann et al. postulates.

scholarly journals Nonmonotonic Reasoning, Expectations Orderings, and Conceptual Spaces

2021 ◽  
Author(s):  
Matías Osta-Vélez ◽  
Peter Gärdenfors
Keyword(s):  
Knowledge Representation ◽  
Nonmonotonic Reasoning ◽  
Knowledge Representation And Reasoning ◽  
Conceptual Spaces ◽  
Reasoning Under Uncertainty ◽  
Consequence Relation ◽  
Nonmonotonic Inference ◽  
Default Logics

AbstractIn Gärdenfors and Makinson (Artif Intell 65(2):197–245, 1994) and Gärdenfors (Knowledge representation and reasoning under uncertainty, Springer-Verlag, 1992) it was shown that it is possible to model nonmonotonic inference using a classical consequence relation plus an expectation-based ordering of formulas. In this article, we argue that this framework can be significantly enriched by adopting a conceptual spaces-based analysis of the role of expectations in reasoning. In particular, we show that this can solve various epistemological issues that surround nonmonotonic and default logics. We propose some formal criteria for constructing and updating expectation orderings based on conceptual spaces, and we explain how to apply them to nonmonotonic reasoning about objects and properties.

scholarly journals Characterization of logic program revision as an extension of propositional revision

2015 ◽  
Vol 16 (1) ◽  
pp. 111-138 ◽  
Author(s):  
NICOLAS SCHWIND ◽  
KATSUMI INOUE
Keyword(s):  
Logic Program ◽  
Nonmonotonic Reasoning ◽  
Knowledge Representation And Reasoning ◽  
Logic Programs ◽  
Characterization Result ◽  
International Conference ◽  
Complete Procedure ◽  
Boolean Lattices ◽  
Computational Properties

AbstractWe address the problem of belief revision of logic programs (LPs), i.e., how to incorporate to a LP P a new LP Q. Based on the structure of SE interpretations, Delgrande et al. (2008. Proc. of the 11th International Conference on Principles of Knowledge Representation and Reasoning (KR'08), 411–421; 2013b. Proc. of the 12th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'13), 264–276) adapted the well-known AGM framework (Alchourrón et al. 1985. Journal of Symbolic Logic 50, 2, 510–530) to LP revision. They identified the rational behavior of LP revision and introduced some specific operators. In this paper, a constructive characterization of all rational LP revision operators is given in terms of orderings over propositional interpretations with some further conditions specific to SE interpretations. It provides an intuitive, complete procedure for the construction of all rational LP revision operators and makes easier the comprehension of their semantic and computational properties. We give a particular consideration to LPs of very general form, i.e., the generalized logic programs (GLPs). We show that every rational GLP revision operator is derived from a propositional revision operator satisfying the original AGM postulates. Interestingly, the further conditions specific to GLP revision are independent from the propositional revision operator on which a GLP revision operator is based. Taking advantage of our characterization result, we embed the GLP revision operators into structures of Boolean lattices, that allow us to bring to light some potential weaknesses in the adapted AGM postulates. To illustrate our claim, we introduce and characterize axiomatically two specific classes of (rational) GLP revision operators which arguably have a drastic behavior. We additionally consider two more restricted forms of LPs, i.e., the disjunctive logic programs (DLPs) and the normal logic programs (NLPs) and adapt our characterization result to disjunctive logic program and normal logic program revision operators.

scholarly journals InfOCF-Web: An Online Tool for Nonmonotonic Reasoning with Conditionals and Ranking Functions

2021 ◽  
Author(s):  
Steven Kutsch ◽  
Christoph Beierle
Keyword(s):  
Nonmonotonic Reasoning ◽  
Minimal Models ◽  
Ranking Functions ◽  
Online Tool ◽  
Ranking Models ◽  
Conditional Knowledge ◽  
System P ◽  
Nonmonotonic Inference

InfOCF-Web provides implementations of system P and system Z inference, and of inference relations based on c-representation with respect to various inference modes and different classes of minimal models. It has an easy-to-use online interface for computing ranking models of a conditional knowledge R, and for answering queries and comparing inference results of nonmonotonic inference relations induced by R.

scholarly journals Data-Driven Metaphor Recognition and Explanation

2013 ◽  
Vol 1 ◽  
pp. 379-390 ◽  
Author(s):  
Hongsong Li ◽  
Kenny Q. Zhu ◽  
Haixun Wang
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Important Task ◽  
Data Driven ◽  
Web Pages ◽  
Inference Mechanism ◽  
Art Methods ◽  
Data Driven Approach ◽  
Machine Reading

Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference mechanism to recognize and explain the metaphors in the text. To our knowledge, this is the first purely data-driven approach of probabilistic metaphor acquisition, recognition, and explanation. Our results shows that it significantly outperforms other state-of-the-art methods in recognizing and explaining metaphors.

Resolving conflicts in knowledge for ambient intelligence

2015 ◽  
Vol 30 (5) ◽  
pp. 455-513 ◽  
Author(s):  
Martin Homola ◽  
Theodore Patkos ◽  
Giorgos Flouris ◽  
Ján Šefránek ◽  
Alexander Šimko ◽  
...  
Keyword(s):  
Ambient Intelligence ◽  
Autonomous Agents ◽  
State Of The Art ◽  
Critical Issue ◽  
Knowledge Representation And Reasoning ◽  
Paraconsistent Reasoning ◽  
Context Modelling ◽  
Long Time ◽  
Theoretical Issues ◽  
Similar Complex

AbstractAmbient intelligence (AmI) proposes pervasive information systems composed of autonomous agents embedded within the environment who, in orchestration, complement human activity in an intelligent manner. As such, it is an interesting and challenging application area for many computer science fields and approaches. A critical issue in such application scenarios is that the agents must be able to acquire, exchange, and evaluate knowledge about the environment, its users, and their activities. Knowledge populated between the agents in such systems may be contextually dependent, ambiguous, and incomplete. Conflicts may thus naturally arise, that need to be dealt with by the agents in an autonomous way. In this survey, we relate AmI to the area of knowledge representation and reasoning (KR), where conflict resolution has been studied for a long time. We take a look at a number of KR approaches that may be applied: context modelling, multi-context systems, belief revision, ontology evolution and debugging, argumentation, preferences, and paraconsistent reasoning. Our main goal is to describe the state of the art in these fields, and to draw attention of researchers to important theoretical issues and practical challenges that still need to be resolved, in order to reuse the results from KR in AmI systems or similar complex and demanding applications.

scholarly journals Uncertainty modelling and computational aspects of data association

2021 ◽  
Vol 31 (5) ◽  
Author(s):  
Jeremie Houssineau ◽  
Jiajie Zeng ◽  
Ajay Jasra
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Possibility Theory ◽  
Uncertainty Modelling ◽  
Alternative Representation ◽  
Lack Of Information ◽  
Partially Observed ◽  
Computational Aspects ◽  
Particle Mcmc ◽  
Filtering Problem

AbstractA novel solution to the smoothing problem for multi-object dynamical systems is proposed and evaluated. The systems of interest contain an unknown and varying number of dynamical objects that are partially observed under noisy and corrupted observations. In order to account for the lack of information about the different aspects of this type of complex system, an alternative representation of uncertainty based on possibility theory is considered. It is shown how analogues of usual concepts such as Markov chains and hidden Markov models (HMMs) can be introduced in this context. In particular, the considered statistical model for multiple dynamical objects can be formulated as a hierarchical model consisting of conditionally independent HMMs. This structure is leveraged to propose an efficient method in the context of Markov chain Monte Carlo (MCMC) by relying on an approximate solution to the corresponding filtering problem, in a similar fashion to particle MCMC. This approach is shown to outperform existing algorithms in a range of scenarios.

scholarly journals Execution of Knowledge-Intensive Processes by Utilizing Ontology-Based Reasoning

2021 ◽  
Author(s):  
Eric Rietzke ◽  
Carsten Maletzki ◽  
Ralph Bergmann ◽  
Norbert Kuhn
Keyword(s):  
Business Process ◽  
Process Model ◽  
State Of The Art ◽  
Inference Mechanism ◽  
Ongoing Research ◽  
New Approach ◽  
The Subject ◽  
Knowledge Intensive ◽  
Process Elements ◽  
Bp Model

AbstractModeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.

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