A new approach to robot path planning using hyperboloid potential functions is presented in this paper. Unlike parabolic potential functions, where the control force increases with distance from the goal and is unbound, and conic potential functions where a singularity occurs at the goal, hyperboloid potential functions avoid both these drawbacks. However, they do combine the advantages of both...

Potential heuristics, recently introduced by Pommerening et al., characterize admissible and consistent heuristics for classical planning as a set of declarative constraints. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original pa...

We show that the imaginary part of the embedding potential, a generalised logarithmic derivative, defined over the interface between an electrical lead and some conductor, has orthogonal eigenfunctions which define conduction channels into and out of the lead. In the case of an infinitely extended interface we establish the relationship between these eigenfunctions and the Bloch states evaluate...

Ahren W. Jasper,† Przemysław Staszewski,‡,§ Graz3 yna Staszewska,‡,| Nathan E. Schultz,† and Donald G. Truhlar*,† Department of Chemistry and Supercomputing Institute, UniVersity of Minnesota, Minneapolis, Minnesota 55455-0431, Department of Theoretical Foundations of Biomedical Sciences and Medical Informatics, Ludwik Rydygier Medical UniVersity, ul. Jagiellońska 13, 85-067 Bydgoszcz, Poland, ...

Predicting protein sequences that fold into specific native three-dimensional structures is a problem of great potential complexity. Although the complete solution is ultimately rooted in understanding the physical chemistry underlying the complex interactions between amino acid residues that determine protein stability, recent work shows that empirical information about these first principles ...

We introduce the concept of Hamiltonian potential function for noncooperative open-loop differential games and characterise necessary and sufficient conditions for its existence. We also identify a class of games admitting a Hamiltonian potential and illustrate appropriate examples pertaining to oligopoly games where price or quantity competition goes along with noncooperative investments eithe...

dominant pole no longer is changing as we add more mutuals) at r 0 =4mm, we have a matrix sparsity of 95.02%. Because of the large size of the full dense partial inductance matrix, we are unable to compare the approximation result with the exact solution. However, we will assert correctness of our result in terms of eigen-value pattern of the partial inductance matrix in Fig.6. Fig.6 gives the ...

Over the last few years we have developed an empirical potential function that solves the protein structure recognition problem: given the sequence for an n-residue globular protein and a collection of plausible protein conformations, including the native conformation for that sequence, identify the correct, native conformation. Having determined this potential on the basis of only some 6500 na...

We survey one area of the emerging field of algorithmic game theory: the use of approximation measures to quantify the inefficiency of game-theoretic equilibria. Potential functions, which enable the application of optimization theory to the study of equilibria, have been a versatile and powerful tool in this area. We use potential functions to bound the inefficiency of equilibria in three dive...

We prove performance guarantees for Bayesian learning algorithms, in particular stochastic model selection, with the help of potential functions. Such a potential quantifies the current state of learning in the system, in a way that the expected error in the next step is bounded by the expected decrease of the potential. For Bayesian stochastic model selection, an appropriate potential function...