Authors:
Juan Carlos Saborío
1
and
Joachim Hertzberg
2
Affiliations:
1
Universität Osnabrück, Germany
;
2
Universität Osnabrück and DFKI Robotics Innovation Center (Osnabrück), Germany
Keyword(s):
Problem Solving, Planning Under Uncertainty, Plan-based Robot Control.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Cognitive Robotics
;
Cognitive Systems
;
Computational Intelligence
;
Evolutionary Computing
;
Informatics in Control, Automation and Robotics
;
Knowledge-Based Systems
;
Robotics and Automation
;
Soft Computing
;
Symbolic Systems
;
Task Planning and Execution
;
Uncertainty in AI
Abstract:
The (PO)MDP framework is a standard model in planning and decision-making under uncertainty, but the
complexity of its methods makes it impractical for any reasonably large problem. In addition, task-planning
demands solutions satisfying efficiency and quality criteria, often unachievable through optimizing methods.
We propose an approach to planning that postpones optimality in favor of faster, satisficing behavior, supported
by context-sensitive assumptions that allow an agent to reduce the dimensionality of its decision problems.We
argue that a practical problem solving agent may sometimes assume full observability and determinism, based
on generalizations, domain knowledge and an attentional filter obtained through a formal understanding of
“relevance”, therefore exploiting the structure of problems and not just their representations.