The goal of this project is to use PRM (probabilistic roadmap) methods to study protein folding. Given a goal (native fold) configuration, we are able to construct a roadmap and derive a set of possible paths for the protein to follow. To do so, we model proteins as multi-link treelike robots with many degrees of freedom. Our work concentrates on improving our techniques for studying the potent...

Our research’s aim is to explore the use of constraint satisfaction techniques in probabilistic planning. We first focus on two special cases that make different assumptions on the observability of the domain: the conformant probabilistic planning problem (CfPP), where the agent’s environment is not observable, and the contingent probabilistic planning problem (CtPP), where the environment is f...

This thesis concerns planning in the K ? IA class of scenarios. We present a space exponential method of doing this and show that in the worst case this complexity is inherent in the problem. Next, we examine some common techniques for increasing the eeciency of classical, search based, regression planning, mainly goal direction, goal decomposition and some domain-dependent heuristics. We show ...

Robotic motion planning is an ubiquitous field of study, with innumerable applications in science, engineering, and beyond. A majority of research has focused on fully automated strategies, which have led to great progress in confronting a variety of interesting scenarios. At its core, however, motion planning is simply infeasible for most complex problems. Automatic planners lack insight and c...

Approaches to computer game playing based on – search of the tree of possible move sequences combined with a position evaluation function have been successful for many games, notably Chess. Such approaches are less successful for games with large search spaces and complex positions, such as Go, and we are led to seek alternatives. One such alternative is to model the goals of the players, and t...

The action language Golog allows specifying the behavior of autonomous systems with very flexible programs that leave certain aspects open to be resolved by the system. Such open aspects are often planning tasks, where the system needs to find a suitable course of actions to reach a given goal. The first part of this thesis aims to make highly efficient planning systems available to the Golog s...

This paper is concerned with modeling p lann ing problems invo lv ing uncerta inty as d iscre tet ime, f in i te -s ta le stochastic au toma ta So lv ing p l ann ing problems is reduced to comp u t i n g policies for Markov decision processes Classical methods for solv ing Markov decision processes cannot cope w i t h the size of the state spaces for typ ica l problems encountered in pract ice ...

Two techniques to provide inferential tractability for decision-theoretic planning are discussed. Knowledge compilation is a speedup technique that pre-computes intermediate quantifies of a domain model to yield a more efficiently executable model. The basic options for compiling decision-theoretic models are defined, leading to the most compiled model in the form of parameterized, conditionact...

Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of action...

The planning of investments when building and upgrading communication networks amounts to notoriously difficult combinatorial problems. Solving these issues can not be done in a straightforward way within a reasonable calculation time, despite the ever-growing computational power of computers and the flexible off-line character of network planning. In this paper, an overview is given of the mea...