We discuss trajectory optimization for hybrid systems with a natural, hierarchical separation of discrete and continuous dynamics. The trajectory optimization problem considered requires that a discrete state sequence and a continuous state trajectory must be both determined to minimize a single cost function, such that the discrete state sequence also solves a symbolic planning problem. We mod...

We consider the partially observable control problem where it is potentially necessary to perform complex information-gathering operations in order to localize state. One approach to solving these problems is to create plans in beliefspace, the space of probability distributions over the underlying state of the system. The belief-space plan encodes a strategy for performing a task while gaining...

Though the planning problem, including the computational complexity of planning , has been long studied in AI, the problem of planning with incomplete information has recieved relatively little attention. Planning with incomplete information may mean any one of a number of diierent things; It may mean that certain facts of the initial state are not known, that plans may contain sensing operatio...

For planning to come of age, plans must be judged by a measure of quality, such as the total cost of actions. This report describes an optimal-cost planner which guarantees global optimality whenever the planning problem has a solution. We code the extraction of an optimal plan, from a planning graph with a fixed number k of levels, as a weighted constraint satisfaction problem (WCSP). The spec...

Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in ...

This paper proposes an optimal approach to infinite-state action planning exploiting automata theory. State sets and actions are characterized by Presburger formulas and represented using minimized finite state machines. The exploration that contributes to the planning via model checking paradigm applies symbolic images in order to compute the deterministic finite automaton for the sets of succ...

State memoization is critical to the good performance of heuristic forward search planners, which represent a significant proportion of the current state-of-the-art planning approaches. In non-temporal planning it is sufficient to discard any state that has been generated before, regardless of the path taken to reach that state, with the only side-constraint being plan cost. We begin this paper...

Background Prior experience and the persisting threat of influenza pandemic indicate the need for global and local preparedness and public health response capacity. The pandemic of 2009 highlighted the importance of such planning and the value of prior efforts at all levels. Our review of the public health response to this pandemic in Pune, India, considers the challenges of integrating global ...

Automated planning has been an active area of research in theoretical computer science and Artificial Intelligence (AI) for over 40 years. Planning is the study of general purpose algorithms that accept as input an initial state, a set of desired goal states, and a planning domain model that describes how actions can transform the state. The problem is to find a sequence of actions that transfo...