Results 1 - 10 of 96

The Use of Explicit Plans to Guide Inductive Proofs

by Alan Bundy - 9TH CONFERENCE ON AUTOMATED DEDUCTION , 1988
"... We propose the use of explicit proof plans to guide the search for a proof in automatic theorem proving. By representing proof plans as the specifications of LCF-like tactics, [Gordon et al 79], and by recording these specifications in a sorted meta-logic, we are able to reason about the conjectures ..."
Abstract - Cited by 295 (40 self) - Add to MetaCart
We propose the use of explicit proof plans to guide the search for a proof in automatic theorem proving. By representing proof plans as the specifications of LCF-like tactics, [Gordon et al 79], and by recording these specifications in a sorted meta-logic, we are able to reason about

Sharing HOL4 and HOL Light proof knowledge

by Thibault Gauthier, Cezary Kaliszyk
"... Abstract. New proof assistant developments often involve concepts sim-ilar to already formalized ones. When proving their properties, a human can often take inspiration from the existing formalized proofs available in other provers or libraries. In this paper we propose and evaluate a num-ber of met ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
of the association between proved theorems and their characteristics to predict the relevant premises. Such external help can be further combined with internal advice. We evaluate the proposed knowledge-sharing methods by reproving the HOL Light and HOL4 stan-dard libraries. The learning-reasoning system HOL

Premise selection and external provers for HOL4

by Thibault Gauthier, Cezary Kaliszyk - In Certified Programs and Proofs (CPP’15), Lecture Notes in Computer Science , 2015
"... Learning-assisted automated reasoning has recently gained popularity among the users of Isabelle/HOL, HOL Light, and Mizar. In this paper, we present an add-on to the HOL4 proof assistant and an adaptation of the HOL(y)Hammer system that provides machine learning-based premise selection and automate ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
Learning-assisted automated reasoning has recently gained popularity among the users of Isabelle/HOL, HOL Light, and Mizar. In this paper, we present an add-on to the HOL4 proof assistant and an adaptation of the HOL(y)Hammer system that provides machine learning-based premise selection

Learning failure in information systems development.

by Kalle Lyytinen , Daniel Robey - Information Systems Journal, , 1999
"... Abstract. Information systems development is a high-risk undertaking, and failures remain common despite advances in development tools and technologies. In this paper, we argue that one reason for this is the collapse of organizational intelligence required to deal with the complexities of systems ..."
Abstract - Cited by 74 (1 self) - Add to MetaCart
they accept and expect poor performance while creating organizational myths that perpetuate short-term optimization. This paper illustrates learning failure in systems development and recommends tactics for overcoming it.

An Algorithm for Learning Tactical Behavior from Observation

by Brian S. Stensrud, Avelino J. Gonzalez
"... This paper describes the components and mechanics of an algorithm that learns context-transition logic for a Context-Based Reasoning [1] model of some tactical behavior. The purpose of the algorithm is to automatically generate a knowledge-base for context-transitions given a sequence of observation ..."
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This paper describes the components and mechanics of an algorithm that learns context-transition logic for a Context-Based Reasoning [1] model of some tactical behavior. The purpose of the algorithm is to automatically generate a knowledge-base for context-transitions given a sequence

Algorithms for Generating Attribute Values for the Classification of Tactical Situations

by David Ezra Sidran, Alberto Maria Segre
"... ABSTRACT: In this paper we describe a series of algorithms that generate real-valued attributes used to classify tactical situations using an unsupervised machine learning system. Attributes for the classification of tactical situations include anchored and unanchored flanks, choke points, restricte ..."
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, restricted avenues of attack and retreat, and interior line of support. 1. Introduction. Our research in Computational Military Tactical Planning (as introduced by Kewley and Embrechts[1]) suggests that, an unsupervised machine learning system (like Gennari and Langley’s ClassIT [2]) can make reasoned

Modeling Human Teaching Tactics in a Computer Tutor

by Mark G. Core, Johanna D. Moore, Claus Zinn, Peter Wiemer-Hastings - In Proceedings of the ITS’00 Workshop on Modelling Human Teaching Tactics and Strategies , 2000
"... Previous psychological research has shown that students must construct knowledge themselves to learn most effectively. This means tutors should not simply give explanations or tell the student the correct answer to a question. Tutors and students should co-construct explanations and tutors should wa ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
walk students through lines of reasoning. These activities unfold over multiple turns and tutors must be flexible enough to deal with (i) failure (students may answer a tutor question wrong or the whole tactic may not be working), (ii) interruptions (students may interrupt with a question) and (iii

Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection using Case-Based Reasoning

by Bryan Ausl, Stephen Lee-urban, Chad Hogg, Héctor Muñoz-avila
"... Abstract. This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changin ..."
Abstract - Cited by 25 (6 self) - Add to MetaCart
Abstract. This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly

Integration of Different Reasoning Modes in a Go Playing and Learning System

by Tristan Cazenave - Proceedings of the AAAI Spring Symposium on Multimodal Reasoning , 1998
"... Integrating multiple reasoning mode is useful in complex domains like the game of Go. Go players use various forms of reasoning during a game. Reasoning at the tactical level is completely different from reasoning at the strategic level. Choosing a plan requires a different form of reasoning th ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Integrating multiple reasoning mode is useful in complex domains like the game of Go. Go players use various forms of reasoning during a game. Reasoning at the tactical level is completely different from reasoning at the strategic level. Choosing a plan requires a different form of reasoning

Integration of Different Reasoning Modes in a Go Playing and Learning

by unknown authors
"... Integrating multiple reasoning mode is useful in complex domains like the game of Go. Go players use various forms of reasoning during a game. Reasoning at the tactical level is completely different from reasoning at the strategic level. Choosing a plan requires a different form of reasoning than kn ..."
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Integrating multiple reasoning mode is useful in complex domains like the game of Go. Go players use various forms of reasoning during a game. Reasoning at the tactical level is completely different from reasoning at the strategic level. Choosing a plan requires a different form of reasoning than
Results 1 - 10 of 96