Commonsense reasoning and commonsense knowledge in artificial intelligence

2015 ◽  
Vol 58 (9) ◽  
pp. 92-103 ◽  
Author(s):  
Ernest Davis ◽  
Gary Marcus
Keyword(s):  
Artificial Intelligence ◽  
Commonsense Reasoning ◽  
Commonsense Knowledge

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.

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.

scholarly journals Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

2019 ◽  
Vol 19 (5-6) ◽  
pp. 1090-1106
Author(s):  
YI WANG ◽  
SHIQI ZHANG ◽  
JOOHYUNG LEE
Keyword(s):  
Planning Under Uncertainty ◽  
Commonsense Reasoning ◽  
Sequential Decision ◽  
Unified Framework ◽  
Probabilistic Planning ◽  
Commonsense Knowledge ◽  
Action Languages ◽  
Action Language ◽  
Markov Decision ◽  
Probabilistic Action

AbstractTo be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsense knowledge. The previous work on this line of research led to the framework called interleaved commonsense reasoning and probabilistic planning (icorpp), which used P-log for representing commmonsense knowledge and Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of icorpp is that its implementation requires non-trivial engineering efforts to bridge the commonsense reasoning and probabilistic planning formalisms. In this paper, we present a unified framework to integrate icorpp’s reasoning and planning components. In particular, we extend probabilistic action language pBC+ to express utility, belief states, and observation as in POMDP models. Inheriting the advantages of action languages, the new action language provides an elaboration tolerant representation of POMDP that reflects commonsense knowledge. The idea led to the design of the system pbcplus2pomdp, which compiles a pBC+ action description into a POMDP model that can be directly processed by off-the-shelf POMDP solvers to compute an optimal policy of the pBC+ action description. Our experiments show that it retains the advantages of icorpp while avoiding the manual efforts in bridging the commonsense reasoner and the probabilistic planner.

Commonsense Knowledge Representation I

2011 ◽  
pp. 327-333 ◽  
Author(s):  
Phillip Ein-Dor
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Highly Qualified ◽  
Commonsense Knowledge ◽  
Medical Diagnoses ◽  
Master Level ◽  
Intelligent Child

Significant advances in artificial intelligence, including machines that play master level chess, or make medical diagnoses, highlight an intriguing paradox. While systems can compete with highly qualified experts in many fields, there has been much less progress in constructing machines that exhibit simple commonsense, the kind expected of any normally intelligent child. As a result, commonsense has been identified as one of the most difficult and important problems in AI (Doyle, 1984; Waltz, 1982).

scholarly journals Essential Features in a Theory of Context for Enabling Artificial General Intelligence

2021 ◽  
Vol 11 (24) ◽  
pp. 11991
Author(s):  
Mayank Kejriwal
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Natural Language ◽  
Language Processing ◽  
Representation Learning ◽  
Machine Intelligence ◽  
General Intelligence ◽  
Commonsense Reasoning ◽  
Core Concept

Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI.

scholarly journals Semantic Analysis of Winograd Schema No. 1

2021 ◽  
Author(s):  
Brandon Bennett
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Semantic Analysis ◽  
Decisive Role ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Reference Resolution ◽  
Commonsense Knowledge ◽  
Key Factor ◽  
General Test

The Winograd Schema Challenge is a general test for Artificial Intelligence, based on problems of pronoun reference resolution. I investigate the semantics and interpretation of Winograd Schemas, concentrating on the original and most famous example. This study suggests that a rich ontology, detailed commonsense knowledge as well as special purpose inference mechanisms are all required to resolve just this one example. The analysis supports the view that a key factor in the interpretation and disambiguation of natural language is the preference for coherence. This preference guides the resolution of co-reference in relation to both explicitly mentioned entities and also implicit entities that are required to form an interpretation of what is being described. I suggest that assumed identity of implicit entities arises from the expectation of coherence and provides a key mechanism that underpins natural language understanding. I also argue that conceptual ontologies can play a decisive role not only in directly determining pronoun references but also in identifying implicit entities and implied relationships that bind together components of a sentence.

scholarly journals MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge

2018 ◽  
Author(s):  
Elizabeth Merkhofer ◽  
John Henderson ◽  
David Bloom ◽  
Laura Strickhart ◽  
Guido Zarrella
Keyword(s):  
Commonsense Reasoning ◽  
Commonsense Knowledge

Logic-Based Reasoning in the Framework of Artificial Intelligence

2010 ◽  
pp. 34-75
Author(s):  
Xenia Naidenova
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Acquisition ◽  
Knowledge Engineering ◽  
Commonsense Reasoning ◽  
Integrated Logic

This chapter focuses on the tasks of knowledge engineering related mainly to knowledge acquisition and modeling integrated logic-based inference. We have overlooked the principal and more important directions of researches that pave the ways to understanding and modeling human plausible (commonsense) reasoning in computers.

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