scholarly journals Logical Formalizations of Commonsense Reasoning: A Survey

2017 ◽  
Vol 59 ◽  
pp. 651-723 ◽  
Author(s):  
Ernest Davis
Keyword(s):  
Artificial Intelligence ◽  
Central Problem ◽  
Logical Inference ◽  
Commonsense Reasoning ◽  
Commonsense Knowledge ◽  
Artificial Intelligence Research ◽  
Representation Language

Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a very difficult one. One approach that has been pursued since the earliest days of the field has been to encode commonsense knowledge as statements in a logic-based representation language and to implement commonsense reasoning as some form of logical inference. This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research.

Applying Commonsense Reasoning to Place Identification

2010 ◽  
Vol 1 (2) ◽  
pp. 36-53 ◽  
Author(s):  
Marco Mamei
Keyword(s):  
Artificial Intelligence ◽  
Mobile Computing ◽  
Real Data ◽  
User Profile ◽  
Gps Data ◽  
Commonsense Reasoning ◽  
Artificial Intelligence System ◽  
Commonsense Knowledge ◽  
Intelligence System ◽  
Place Identification

Recent mobile computing applications try to automatically identify the places visited by the user from a log of GPS readings. Such applications reverse geocode the GPS data to discover the actual places (shops, restaurants, etc.) where the user has been. Unfortunately, because of GPS errors, the actual addresses and businesses being visited cannot be extracted unambiguously and often only a list of candidate places can be obtained. Commonsense reasoning can notably help the disambiguation process by invalidating some unlikely findings (e.g., a user visiting a cinema in the morning). This paper illustrates the use of Cyc—an artificial intelligence system comprising a database of commonsense knowledge—to improve automatic place identification. Cyc allows to probabilistically rank the list of candidate places in consideration of the commonsense likelihood of that place being actually visited on the basis of the user profile, the time of the day, what happened before, and so forth. The system has been evaluated using real data collected from a mobile computing application.

Solving the Frame Problem Socially

2018 ◽  
Author(s):  
Harry Halpin
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Knowledge Representation ◽  
Formal System ◽  
Philosophical Investigation ◽  
Logical Inference ◽  
Frame Problem ◽  
Artificial Intelligence Research ◽  
Technological Means ◽  
The Frame Problem

The question of how technology impacts the existing forms of epistemology and forms a new kind of socially extended epistemology deserves a thorough philosophical investigation. Traditionally, epistemology has been bound to a vision of knowledge as internal beliefs justified via logical inference. This view was externalized by artificial intelligence research into knowledge representation. Yet historically this form of research has failed, with knowledge representation being unable to cope with the Frame Problem: How to capture a changing and fluid world in a formal system that can be mechanized? Today, people use search engines, tagging, and social media to leave an enactive “social trail” through the vast amount of information, creating new kinds of distributed and extended knowledge that challenges traditional theories of epistemology. This shaping of the epistemic environment allows humans to socially solve the Frame Problem and extend the bounds of knowledge via technological means.

Applying Commonsense Reasoning to Place Identification

2012 ◽  
pp. 951-968
Author(s):  
Marco Mamei
Keyword(s):  
Artificial Intelligence ◽  
Mobile Computing ◽  
Real Data ◽  
User Profile ◽  
Gps Data ◽  
Commonsense Reasoning ◽  
Artificial Intelligence System ◽  
Commonsense Knowledge ◽  
Intelligence System ◽  
Place Identification

Recent mobile computing applications try to automatically identify the places visited by the user from a log of GPS readings. Such applications reverse geocode the GPS data to discover the actual places (shops, restaurants, etc.) where the user has been. Unfortunately, because of GPS errors, the actual addresses and businesses being visited cannot be extracted unambiguously and often only a list of candidate places can be obtained. Commonsense reasoning can notably help the disambiguation process by invalidating some unlikely findings (e.g., a user visiting a cinema in the morning). This paper illustrates the use of Cyc—an artificial intelligence system comprising a database of commonsense knowledge—to improve automatic place identification. Cyc allows to probabilistically rank the list of candidate places in consideration of the commonsense likelihood of that place being actually visited on the basis of the user profile, the time of the day, what happened before, and so forth. The system has been evaluated using real data collected from a mobile computing application.

Applying Commonsense Reasoning to Place Identification

2012 ◽  
pp. 124-140
Author(s):  
Marco Mamei
Keyword(s):  
Artificial Intelligence ◽  
Mobile Computing ◽  
Real Data ◽  
User Profile ◽  
Gps Data ◽  
Commonsense Reasoning ◽  
Artificial Intelligence System ◽  
Commonsense Knowledge ◽  
Intelligence System ◽  
Place Identification

Recent mobile computing applications try to automatically identify the places visited by the user from a log of GPS readings. Such applications reverse geocode the GPS data to discover the actual places (shops, restaurants, etc.) where the user has been. Unfortunately, because of GPS errors, the actual addresses and businesses being visited cannot be extracted unambiguously and often only a list of candidate places can be obtained. Commonsense reasoning can notably help the disambiguation process by invalidating some unlikely findings (e.g., a user visiting a cinema in the morning). This paper illustrates the use of Cyc—an artificial intelligence system comprising a database of commonsense knowledge—to improve automatic place identification. Cyc allows to probabilistically rank the list of candidate places in consideration of the commonsense likelihood of that place being actually visited on the basis of the user profile, the time of the day, what happened before, and so forth. The system has been evaluated using real data collected from a mobile computing application.

A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

2002 ◽  
Vol 10 (03) ◽  
pp. 281-307 ◽  
Author(s):  
TRU H. CAO
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Functional Relation ◽  
Computing With Words ◽  
Conceptual Graphs ◽  
Graph Language ◽  
Conceptual Graph ◽  
Knowledge Representation Language ◽  
Representation Language

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

scholarly journals RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Elin Trägårdh ◽  
Pablo Borrelli ◽  
Reza Kaboteh ◽  
Tony Gillberg ◽  
Johannes Ulén ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Medicine ◽  
Artificial Intelligence Research
Sign in / Sign up

Export Citation Format

Share Document