Addressing the Qualification Problem in FLUX

Authors:
Yves Martin;Michael Thielscher
Affiliations:
-;-
Venue:
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Year:
2001

Citing 10
Cited 4

Applications of circumscription to formalizing common-sense knowledge

Artificial Intelligence
Foundations of logic programming; (2nd extended ed.)

Foundations of logic programming; (2nd extended ed.)
Ramification and causality

Artificial Intelligence
From situation calculus to fluent calculus: state update axioms as a solution to the inferential frame problem

Artificial Intelligence
The Qualification Problem: A solution to the problem of anomalous models

Artificial Intelligence
Adding Priorities and Specificity to Default Logic

JELIA '94 Proceedings of the European Workshop on Logics in Artificial Intelligence
Event Calculus Planning Revisited

ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
STRIPS: a new approach to the application of theorem proving to problem solving

IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Epistemological problems of artificial intelligence

IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Formal theories of action (preliminary report)

IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2

The Qualification Problem: A solution to the problem of anomalous models

Artificial Intelligence
Intelligent execution monitoring in dynamic environments

Fundamenta Informaticae
FLUX: A logic programming method for reasoning agents

Theory and Practice of Logic Programming
Intelligent Execution Monitoring in Dynamic Environments

Fundamenta Informaticae - The 1st International Workshop on Knowledge Representation and Approximate Reasoning (KR&AR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Qualification Problem arises for planning agents in real-world environments, where unexpected circumstances may at any time prevent the successful performance of an action. We present a logic programming method to cope with the Qualification Problem in the action programming language Flux, which builds on the Fluent Calculus as a solution to the fundamental Frame Problem. Our system allows to plan under the default assumption that actions succeed as they normally do, and to reason about these assumptions in order to recover from unexpected action failures.