Commonsense Reasoning Using Theorem Proving and Machine Learning

2019 ◽  
pp. 395-413 ◽  
Author(s):  
Sophie Siebert ◽  
Claudia Schon ◽  
Frieder Stolzenburg
Keyword(s):  
Machine Learning ◽  
Theorem Proving ◽  
Commonsense Reasoning

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.

scholarly journals CoRg: Commonsense Reasoning Using a Theorem Prover and Machine Learning

10.29007/lt5p ◽  
2019 ◽  
Author(s):  
Sophie Siebert ◽  
Frieder Stolzenburg
Keyword(s):  
Machine Learning ◽  
Question Answering ◽  
Learning Algorithms ◽  
Knowledge Bases ◽  
Black Box ◽  
Machine Learning Algorithms ◽  
Theorem Prover ◽  
Commonsense Reasoning ◽  
Probable Answer ◽  
Everyday Task

Commonsense reasoning is an everyday task that is intuitive for humans but hard to implement for computers. It requires large knowledge bases to get the required data from, although this data is still incomplete or even inconsistent. While machine learning algorithms perform rather well on these tasks, the reasoning process remains a black box. To close this gap, our system CoRg aims to build an explainable and well-performing system, which consists of both an explainable deductive derivation process and a machine learning part. We conduct our experiments on the Copa question-answering benchmark using the ontologies WordNet, Adimen-SUMO, and ConceptNet. The knowledge is fed into the theorem prover Hyper and in the end the conducted models will be analyzed using machine learning algorithms, to derive the most probable answer.

scholarly journals Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases

2019 ◽  
Vol 33 ◽  
pp. 1360-1367
Author(s):  
Abhishek Sharma ◽  
Keith M. Goolsbey
Keyword(s):  
Machine Learning ◽  
Question Answering ◽  
Knowledge Bases ◽  
Cognitive Systems ◽  
General Knowledge ◽  
Commonsense Reasoning ◽  
Selection Methods ◽  
Monte Carlo Simulation Technique ◽  
Simulation Based ◽  
Limited Reasoning

Cognitive systems must reason with large bodies of general knowledge to perform complex tasks in the real world. However, due to the intractability of reasoning in large, expressive knowledge bases (KBs), many AI systems have limited reasoning capabilities. Successful cognitive systems have used a variety of machine learning and axiom selection methods to improve inference. In this paper, we describe a search heuristic that uses a Monte-Carlo simulation technique to choose inference steps. We test the efficacy of this approach on a very large and expressive KB, Cyc. Experimental results on hundreds of queries show that this method is highly effective in reducing inference time and improving question-answering (Q/A) performance.

Machine Learning for First-Order Theorem Proving

2014 ◽  
Vol 53 (2) ◽  
pp. 141-172 ◽  
Author(s):  
James P. Bridge ◽  
Sean B. Holden ◽  
Lawrence C. Paulson
Keyword(s):  
Machine Learning ◽  
Theorem Proving ◽  
First Order

Machine Learning as a Commonsense Reasoning Process

2009 ◽  
pp. 605-611
Author(s):  
Xenia Naidenova
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Query Processing ◽  
Inductive Reasoning ◽  
Commonsense Reasoning ◽  
Logical Language ◽  
Knowledge Reasoning ◽  
Inductive Machine Learning ◽  
Database Technology ◽  
Logical Rules

One of the most important tasks in database technology is to combine the following activities: data mining or inferring knowledge from data and query processing or reasoning on acquired knowledge. The solution of this task requires a logical language with unified syntax and semantics for integrating deductive (using knowledge) and inductive (acquiring knowledge) reasoning. In this paper, we propose a unified model of commonsense reasoning. We also demonstrate that a large class of inductive machine learning (ML) algorithms can be transformed into the commonsense reasoning processes based on wellknown deduction and induction logical rules. The concept of a good classification (diagnostic) test (Naidenova & Polegaeva, 1986) is the basis of our approach to combining deductive and inductive reasoning.

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