A Critical Look at the Abstraction Based on Macro-Operators

Abstraction can be an effective technique for dealing with the complexity of planning tasks. This paper is aimed at assessing and identifying in which cases abstraction can actually speed-up the overall search. In fact, it is well known that the impact of abstraction on the time spent to search for a solution of a planning problem can be positive or negative, depending on several factors -including the number of objects defined in the domain, the branching factor, and the plan length. Experimental results highlight the role of such aspects on the overall performance of an algorithm that performs the search at the ground-level only, and compares them with the ones obtained by enforcing abstraction

Process Calculi Abstractions for Biology

Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri Nets theory, and some other by stochastic processes. A most recent approach consists in interpreting the living entities as terms of process calculi where the behavior of the represented systems can be inferred by applying syntax-driven rules. A comprehensive picture of the state of the art of the process calculi approach to biological modeling is still missing. This paper goes in the direction of providing such a picture by presenting a comparative survey of the process calculi that have been used and proposed to describe the behavior of living entities. This is the preliminary version of a paper that was published in Algorithmic Bioprocesses. The original publication is available at http://www.springer.com/computer/foundations/book/978-3-540-88868-

Extending the use of plateau-escaping macro-actions in planning

Many fully automated planning systems use a single, domain independent heuristic to guide search and no other problem specific guidance. While these systems exhibit excellent performance, they are often out-performed by systems which are either given extra human-encoded search information, or spend time learning additional search control information offline. The benefit of systems which do not require human intervention is that they are much closer to the ideal of autonomy. This document discusses a system which learns additional control knowledge, in the form of macro-actions, during planning, without the additional time required for an online learning step. The results of various techniques for managing the collection of macro-actions generated are also discussed. Finally, an explanation of the extension of the techniques to other planning systems is presented