TY - GEN
T1 - Generating corrective explanations for interactive constraint satisfaction
AU - O'Callaghan, Barry
AU - O'Sullivan, Barry
AU - Freuder, Eugene C.
PY - 2005
Y1 - 2005
N2 - Interactive tasks such as online configuration and e-commerce can be modelled as constraint satisfaction problems (CSPs). These can be solved interactively by a user assigning values to variables. The user may require advice and explanations from a system to help him/her find a satisfactory solution. Explanations of failure in constraint programming tend to focus on conflict. However, what is really desirable is an explanation that is corrective in the sense that it provides the basis for moving forward in the problem-solving process, More specifically, when faced with a dead-end, or when a desirable value has been removed from a domain, we need to compute alternative assignments for a subset of the assigned variables that enables the user to move forward. This paper defines this notion of corrective explanation, and proposes an algorithm to generate such explanations. The approach is shown to perform well on both real-world configuration benchmarks and randomly generated problems.
AB - Interactive tasks such as online configuration and e-commerce can be modelled as constraint satisfaction problems (CSPs). These can be solved interactively by a user assigning values to variables. The user may require advice and explanations from a system to help him/her find a satisfactory solution. Explanations of failure in constraint programming tend to focus on conflict. However, what is really desirable is an explanation that is corrective in the sense that it provides the basis for moving forward in the problem-solving process, More specifically, when faced with a dead-end, or when a desirable value has been removed from a domain, we need to compute alternative assignments for a subset of the assigned variables that enables the user to move forward. This paper defines this notion of corrective explanation, and proposes an algorithm to generate such explanations. The approach is shown to perform well on both real-world configuration benchmarks and randomly generated problems.
UR - https://www.scopus.com/pages/publications/33646201119
U2 - 10.1007/11564751_34
DO - 10.1007/11564751_34
M3 - Conference proceeding
AN - SCOPUS:33646201119
SN - 3540292381
SN - 9783540292388
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 445
EP - 459
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 11th International Conference on Principles and Practice of Constraint Programming - CP 2005
Y2 - 1 October 2005 through 5 October 2005
ER -