Abstract--The problem of state estimation with initial state uncertainty is approached from a statistical decision theory point of view. The initial state is regarded as determinis...
We present a general algorithm for synthesizing state invariants that speed up automated planners and have other applications in reasoning about change. Invariants are facts that ...
Wedescribe somenewpreprocessing techniques that enable faster domain-independentplanning. Thefirst set of techniquesis aimedat inferring state constraints from the structure of pl...
This paper presents a system that controls the behavior of a mobile robot. The system is based on situation calculus, the initial state is described and a goal is given, Prolog pro...
Markov Decision Processes (MDP) have been widely used as a framework for planning under uncertainty. They allow to compute optimal sequences of actions in order to achieve a given...
Traditional minimum area retiming algorithms attempt to achieve their prescribed objective with no regard to maintaining the initial state of the system. This issue is important f...
Retiming is a transformation that optimizes a sequential circuit by relocating the registers. When the circuit has an initial state, one must compute an equivalent initial state f...
We present a time and energy optimal controller for a two-wheeled differentially driven robot. We call a mission the task of bringing the robot from an initial state to a desired f...
Conformant planning is a variation of classical AI planning where the initial state is partially known and actions can have nondeterministic effects. While a classical plan must a...
We propose a new self-stabilizing anonymous leader election algorithm in a tree graph. We show the correctness of the protocol and show that the protocol terminates in O(n4 ) time...