In the last years, some very promising domain independent heuristic state-space planners for STRIPS worlds, like ASP/HSP, HSPr and GRT, have been presented. These planners achieve remarkable performance in some domains, like the blocks world, the logistics and the gripper, but they are not effective in other domains, like the grid and the mystery. In this paper we propose the use of state const...

We propose an approach to study static properties of metabolic networks with genetic regulation. We base our results on differential inequalities which are constraints on the values of the partial derivatives of the reaction rate functions. The approach uses an iterative elimination method for the steady state equations involving algebraic modules that satisfy the Gale-Nikaido global univalence...

This paper discusses the estimation of time-dependent utility that is used for decision-theoretic planning in dynamic environments. A general approach to modeling the time-dependent utility is presented which is based on the decomposition of time-dependent utility into time-dependent state description (SEM: situation evolution model) and state-dependent utility description (PUM: plan utility mo...

Multi-robot path planning is difficult due to the combinatorial explosion of the search space with every new robot added. Complete search of the combined state-space soon becomes intractable. In this paper we present a novel form of abstraction that allows us to plan much more efficiently. The key to this abstraction is the partitioning of the map into subgraphs of known structure with entry an...

This paper is a step toward understanding the algorithmic concomitants of modeling robots as mobile finite-state machines (FSMs, for short) that travel within square two-dimensional meshes (which abstract the floors of laboratories or factories or warehouses). We study the ability of FSMs to scalably perform a simple path-planning task called parking, within fixed square meshes of arbitrary siz...

We present a technique which allows partial-order causallink (POCL) planning systems to use heuristics known from state-based planning to guide their search. The technique encodes a given partially ordered partial plan as a new classical planning problem that yields the same set of solutions reachable from the given partial plan. As heuristic estimate of the given partial plan a state-based heu...

The introduction of the concept of state novelty has advanced the state of the art in deterministic online planning in Atarilike problems and in planning with rewards in general, when rewards are defined on states. In classical planning, however, the success of novelty as the dichotomy between novel and non-novel states was somewhat limited. Until very recently, novelty-based methods were not a...

One of the most successful approaches in automated planning is to use heuristic state-space search. A popular heuristic that is used by a number of state-space planners is based on relaxing the planning task by ignoring the delete effects of the actions. In several planning domains, however, this relaxation produces rather weak estimates to guide search effectively. We present a relaxation usin...

We present a plan representation and a generalized algorithm template, called UCP, for unifying the classical plan-space and state-space planning approaches within a single framework. UCP models planning as a process of refining a partial plan. The plan-space and state-space planning approaches are cast as complementary refinement strategies operating on the same partial plan representation. UC...

This paper presents an approach of motion planning for a humanoid robot using a state transition method. In this method, motion planning is simplified by introducing a state-space to describe the whole motion series. And each state in the state-space corresponds to a contact state specified during the motion. The continuous motion is represented by a sequence of discrete states. The concept of ...