16,370 research outputs found
Linear Tabulated Resolution Based on Prolog Control Strategy
Infinite loops and redundant computations are long recognized open problems
in Prolog. Two ways have been explored to resolve these problems: loop checking
and tabling. Loop checking can cut infinite loops, but it cannot be both sound
and complete even for function-free logic programs. Tabling seems to be an
effective way to resolve infinite loops and redundant computations. However,
existing tabulated resolutions, such as OLDT-resolution, SLG- resolution, and
Tabulated SLS-resolution, are non-linear because they rely on the
solution-lookup mode in formulating tabling. The principal disadvantage of
non-linear resolutions is that they cannot be implemented using a simple
stack-based memory structure like that in Prolog. Moreover, some strictly
sequential operators such as cuts may not be handled as easily as in Prolog.
In this paper, we propose a hybrid method to resolve infinite loops and
redundant computations. We combine the ideas of loop checking and tabling to
establish a linear tabulated resolution called TP-resolution. TP-resolution has
two distinctive features: (1) It makes linear tabulated derivations in the same
way as Prolog except that infinite loops are broken and redundant computations
are reduced. It handles cuts as effectively as Prolog. (2) It is sound and
complete for positive logic programs with the bounded-term-size property. The
underlying algorithm can be implemented by an extension to any existing Prolog
abstract machines such as WAM or ATOAM.Comment: To appear as the first accepted paper in Theory and Practice of Logic
Programming (http://www.cwi.nl/projects/alp/TPLP
Program transformations using temporal logic side conditions
This paper describes an approach to program optimisation based on transformations, where temporal logic is used to specify side conditions, and strategies are created which expand the repertoire of transformations and provide a suitable level of abstraction. We demonstrate the power of this approach by developing a set of optimisations using our transformation language and showing how the transformations can be converted into a form which makes it easier to apply them, while maintaining trust in the resulting optimising steps. The approach is illustrated through a transformational case study where we apply several optimisations to a small program
Stop It, and Be Stubborn!
A system is AG EF terminating, if and only if from every reachable state, a
terminal state is reachable. This publication argues that it is beneficial for
both catching non-progress errors and stubborn set state space reduction to try
to make verification models AG EF terminating. An incorrect mutual exclusion
algorithm is used as an example. The error does not manifest itself, unless the
first action of the customers is modelled differently from other actions. An
appropriate method is to add an alternative first action that models the
customer stopping for good. This method typically makes the model AG EF
terminating. If the model is AG EF terminating, then the basic strong stubborn
set method preserves safety and some progress properties without any additional
condition for solving the ignoring problem. Furthermore, whether the model is
AG EF terminating can be checked efficiently from the reduced state space
Energy-Efficient Algorithms
We initiate the systematic study of the energy complexity of algorithms (in
addition to time and space complexity) based on Landauer's Principle in
physics, which gives a lower bound on the amount of energy a system must
dissipate if it destroys information. We propose energy-aware variations of
three standard models of computation: circuit RAM, word RAM, and
transdichotomous RAM. On top of these models, we build familiar high-level
primitives such as control logic, memory allocation, and garbage collection
with zero energy complexity and only constant-factor overheads in space and
time complexity, enabling simple expression of energy-efficient algorithms. We
analyze several classic algorithms in our models and develop low-energy
variations: comparison sort, insertion sort, counting sort, breadth-first
search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL
trees, binary heaps, and dynamic arrays. We explore the time/space/energy
trade-off and develop several general techniques for analyzing algorithms and
reducing their energy complexity. These results lay a theoretical foundation
for a new field of semi-reversible computing and provide a new framework for
the investigation of algorithms.Comment: 40 pages, 8 pdf figures, full version of work published in ITCS 201
CIRCAL-2 - A general-purpose on-line circuit-design program. User's manual
Users manual describing general purpose, on-line circuit analysis and design computer progra
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
- ā¦