Visual commonsense reasoning with directional visual connections

2021 ◽  
Vol 22 (5) ◽  
pp. 625-637
Author(s):  
Yahong Han ◽  
Aming Wu ◽  
Linchao Zhu ◽  
Yi Yang
Keyword(s):  
Commonsense Reasoning

Formalization of Inheritance Reasoning in Autoepistemic Logic

1990 ◽  
Vol 13 (4) ◽  
pp. 403-443
Author(s):  
Michael Gelfond ◽  
Halina Przymusinska
Keyword(s):  
General Framework ◽  
Nonmonotonic Reasoning ◽  
Commonsense Reasoning ◽  
Autoepistemic Logic

Current research in the area of nonmonotonic reasoning suggests that autoepistemic logic provides a general framework for formalizing commonsense reasoning in various domains of discourse. The goal of this paper is to investigate the suitability of autoepistemic logic for formalization of some forms of inheritance reasoning. To this end we propose a new semantics for inheritance networks with exceptions based on autoepistemic logic.

scholarly journals COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

2021 ◽  
Author(s):  
Shikhar Singh ◽  
Nuan Wen ◽  
Yu Hou ◽  
Pegah Alipoormolabashi ◽  
Te-lin Wu ◽  
...  
Keyword(s):  
Commonsense Reasoning

scholarly journals Commonsense Reasoning to Guide Deep Learning for Scene Understanding (Extended Abstract)

2020 ◽  
Author(s):  
Mohan Sridharan ◽  
Tiago Mota
Keyword(s):  
Domain Knowledge ◽  
Inductive Learning ◽  
Network Models ◽  
Computational Effort ◽  
Partial Occlusion ◽  
Commonsense Reasoning ◽  
Deep Networks ◽  
Training Examples ◽  
The Stability ◽  
Simulated Images

Our architecture uses non-monotonic logical reasoning with incomplete commonsense domain knowledge, and incremental inductive learning, to guide the construction of deep network models from a small number of training examples. Experimental results in the context of a robot reasoning about the partial occlusion of objects and the stability of object configurations in simulated images indicate an improvement in reliability and a reduction in computational effort in comparison with an architecture based just on deep networks.

scholarly journals Interactive Fiction Games: A Colossal Adventure

2020 ◽  
Vol 34 (05) ◽  
pp. 7903-7910
Author(s):  
Matthew Hausknecht ◽  
Prithviraj Ammanabrolu ◽  
Marc-Alexandre Côté ◽  
Xingdi Yuan
Keyword(s):  
Autonomous Agents ◽  
Rapid Development ◽  
Human Intelligence ◽  
Commonsense Reasoning ◽  
Interactive Fiction ◽  
Language Understanding ◽  
Effect Change ◽  
Development Of Language ◽  
Action Spaces ◽  
Comprehensive Study

A hallmark of human intelligence is the ability to understand and communicate with language. Interactive Fiction games are fully text-based simulation environments where a player issues text commands to effect change in the environment and progress through the story. We argue that IF games are an excellent testbed for studying language-based autonomous agents. In particular, IF games combine challenges of combinatorial action spaces, language understanding, and commonsense reasoning. To facilitate rapid development of language-based agents, we introduce Jericho, a learning environment for man-made IF games and conduct a comprehensive study of text-agents across a rich set of games, highlighting directions in which agents can improve.

Interconnecting a Class of Machine Learning Algorithms with Logical Commonsense Reasoning Operations

2010 ◽  
pp. 214-246
Author(s):  
Xenia Naidenova
Keyword(s):  
Machine Learning ◽  
Large Class ◽  
Diagnostic Tests ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Problems ◽  
Mathematical Language ◽  
Commonsense Reasoning ◽  
Good Classification ◽  
Logical Rules

The purpose of this chapter is to demonstrate the possibility of transforming a large class of machine learning algorithms into commonsense reasoning processes based on using well-known deduction and induction logical rules. The concept of a good classification (diagnostic) test for a given set of positive examples lies in the basis of our approach to the machine learning problems. The task of inferring all good diagnostic tests is formulated as searching the best approximations of a given classification (a partitioning) on a given set of examples. The lattice theory is used as a mathematical language for constructing good classification tests. The algorithms of good tests inference are decomposed into subtasks and operations that are in accordance with main human commonsense reasoning rules.

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