Add to ORKGAs AI becomes more ubiquitous there is increasing interest in computers being able to provide explanations for their conclusions. This paper proposes exploring the relationship between the structure of a problem and its explanation. The nature of this challenge is introduced through a series of simple constraint satisfaction problems
AbstractThe ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have develop...
Research in constraint programming typically focuses on problem solving efficiency. However, the way...
: This paper describes a framework for expressing and solving combinatorial problems. The framework ...
As AI becomes more ubiquitous there is increasing interest in computers being able to provide explan...
In this paper we present a general representation for constraint satisfaction problems (CSP) and a -...
Human-aware AI is increasingly important as AI becomes more powerful and ubiquitous. A good foundat...
The need to derive explanations from machine learning (ML)-based AI systems has been addressed in re...
Dottorato di Ricerca in Ingegneria dei Sistemi e Informatica XXVIII Ciclo, a.a. 2015-2016A fundament...
International audienceIdentifying structures in a given combinatorial problem is often a key step fo...
Constraint programming is a search paradigm for solving combinatorial optimization pro- blems, that ...
Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting user...
The aim of this paper is to identify and to characterize the features that render one class of the C...
Constraint programming is a research topic benefiting from many other areas: discrete mathematics, n...
The Constraint Satisfaction Problem (CSP) is ubiquitous in artificial intelligence. It has a wide ap...
The ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have developed indep...
AbstractThe ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have develop...
Research in constraint programming typically focuses on problem solving efficiency. However, the way...
: This paper describes a framework for expressing and solving combinatorial problems. The framework ...
As AI becomes more ubiquitous there is increasing interest in computers being able to provide explan...
In this paper we present a general representation for constraint satisfaction problems (CSP) and a -...
Human-aware AI is increasingly important as AI becomes more powerful and ubiquitous. A good foundat...
The need to derive explanations from machine learning (ML)-based AI systems has been addressed in re...
Dottorato di Ricerca in Ingegneria dei Sistemi e Informatica XXVIII Ciclo, a.a. 2015-2016A fundament...
International audienceIdentifying structures in a given combinatorial problem is often a key step fo...
Constraint programming is a search paradigm for solving combinatorial optimization pro- blems, that ...
Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting user...
The aim of this paper is to identify and to characterize the features that render one class of the C...
Constraint programming is a research topic benefiting from many other areas: discrete mathematics, n...
The Constraint Satisfaction Problem (CSP) is ubiquitous in artificial intelligence. It has a wide ap...
The ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have developed indep...
AbstractThe ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have develop...
Research in constraint programming typically focuses on problem solving efficiency. However, the way...
: This paper describes a framework for expressing and solving combinatorial problems. The framework ...