Delayed predicate algorithm evaluation in hybrid answer set programming (ASP)

A hybrid answer set programming (ASP) solver is a logic programming type solver with a variety of applications, e.g., diagnosing software failures, modeling dynamical systems, etc. A hybrid ASP solver finds solutions for hybrid ASP programs, which are sets of hybrid ASP rules. Hybrid ASP rules include stationary rules with associated predicate algorithms and advancing rules with associated advancing algorithms. Solutions to hybrid ASP programs are found iteratively by starting with an initial state and producing consequent states. Part of computing a consequent state involves determination of the stationary rules that are applicable at the consequent state. This in turn involves evaluating predicate algorithms associated with the rules. This approach is inefficient, since it requires that all the predicate algorithms be evaluated in all the states. The techniques of this disclosure avoid the inefficiency of evaluating predicate algorithms by adding auxiliary rules that enable intelligent guesses as to whether an algorithm will accept or reject, in turn enabling the continued evaluation of the rules without the evaluation of the algorithms

Hybrid ASP

This paper introduces an extension of Answer Set Programming (ASP) called Hybrid ASP which will allow the user to reason about dynamical systems that exhibit both discrete and continuous aspects. The unique feature of Hybrid ASP is that it allows the use of ASP type rules as controls for when to apply algorithms to advance the system to the next position. That is, if the prerequisites of a rule are satisfied and the constraints of the rule are not violated, then the algorithm associated with the rule is invoked

Extensions of Answer Set Programming

This work discusses two new extensions of Answer Set Programming (ASP) and a new computational method for solving the following two problems : (1) given a finite propositional logic program P which has a stable model, find a stable model M of P, and (2) given a finite propositional logic program P which has no stable model, find a maximal program P' which is a subset of P which has a stable model and find a stable model M' of P'. The first extension called Preference Set Constraint (PSC) programming extends the Set Constraint programming introduced by Marek and Remmel to allow reasoning with preferences. The generality of PSC programming is demonstrated by showing that PSC programming can be used to express optimal stable models of Answer Set Optimization (ASO) of Brewka, Niemela and Truszczynski; general preferences of Son and Pontelli. An extension of PSC programming can be used to express preferred answer sets and weakly preferred answer sets of Brewka and Eiter. It is proven that under mild assumptions the problem of determining whether M is a preferred PSC stable model of a PSC program P is CoNP-complete. The second extension called Hybrid ASP (H-ASP) allows to combine logical reasoning and numerical algorithms. One of the goals of H- ASP is to allow users to reason about dyamical systems that exhibit both continuous and discrete aspects. In this work it is shown how H-ASP can be used to compute a finite horizon optimal strategy for an agent acting in a dynamic domain. The discussion leads to an introduction of a new programming language H-ASP#. It is proven that the computational complexity of H-ASP# program W# is EXP- complete in the length of W#. The new computational method for solving the above mentioned two problems called the Metropolized Forward Chaining (MFC) algorithm is based on combining the Metropolis algorithm and the Forward Chaining algorithm of Marek, Nerode and Remmel. The use of Stochastic Approximation Monte Carlo (SAMC) algorithm of in the MFC instead of the Metropolis algorithm is discussed. The results of the computational experiments conducted with the two versions of MFC are reported. Values for some of the parameters to be used with MFC are suggested. An asymptotic result for certain choices of parameters is prove

Extensions of Answer Set Programming

This work discusses two new extensions of Answer Set Programming (ASP) and a new computational method for solving the following two problems : (1) given a finite propositional logic program P which has a stable model, find a stable model M of P, and (2) given a finite propositional logic program P which has no stable model, find a maximal program P' which is a subset of P which has a stable model and find a stable model M' of P'. The first extension called Preference Set Constraint (PSC) programming extends the Set Constraint programming introduced by Marek and Remmel to allow reasoning with preferences. The generality of PSC programming is demonstrated by showing that PSC programming can be used to express optimal stable models of Answer Set Optimization (ASO) of Brewka, Niemela and Truszczynski; general preferences of Son and Pontelli. An extension of PSC programming can be used to express preferred answer sets and weakly preferred answer sets of Brewka and Eiter. It is proven that under mild assumptions the problem of determining whether M is a preferred PSC stable model of a PSC program P is CoNP-complete. The second extension called Hybrid ASP (H-ASP) allows to combine logical reasoning and numerical algorithms. One of the goals of H- ASP is to allow users to reason about dyamical systems that exhibit both continuous and discrete aspects. In this work it is shown how H-ASP can be used to compute a finite horizon optimal strategy for an agent acting in a dynamic domain. The discussion leads to an introduction of a new programming language H-ASP#. It is proven that the computational complexity of H-ASP# program W# is EXP- complete in the length of W#. The new computational method for solving the above mentioned two problems called the Metropolized Forward Chaining (MFC) algorithm is based on combining the Metropolis algorithm and the Forward Chaining algorithm of Marek, Nerode and Remmel. The use of Stochastic Approximation Monte Carlo (SAMC) algorithm of in the MFC instead of the Metropolis algorithm is discussed. The results of the computational experiments conducted with the two versions of MFC are reported. Values for some of the parameters to be used with MFC are suggested. An asymptotic result for certain choices of parameters is prove

WIPACrepo/iceprod: v2.5.4

<p>Several minor features for usability, and support for SuperComputing demo v2. This is the code that was used during the demo on 2020-02-04.</p> <p>Features:</p> <ul> <li>c882ff8: Add a 'configs' module option, to write out a json config file into the module working directory</li> <li>aa23eaa: Allow website access to past 10 logs instead of only one</li> <li>6a63061: Allow filtering and projection for /pilots, and use it in grid commands </li> </ul> <p>Bugfixes:</p> <ul> <li>26b4165: Fix stdout/stderr not being recorded</li> <li>4d86e21: No more duplicate pilot ids, so we don't delete a pilot every cycle</li> </ul&gt

WIPACrepo/iceprod: v2.5.2

<p>Support for Graham supercomputer in ComputeCanada</p> <p>Features:</p> <ul> <li>#88: initial support for supercomputer mode</li> <li>19b30a8: let the pilot delete itself to improve monitoring responsiveness</li> <li>3ea4166: add a script for running production tasks manually, given the dataset and job numbers</li> </ul> <p>Bugfixes:</p> <ul> <li>92ce4ff: fix task iter when processing multiple tasks in parallel</li> <li>70aa0c6: try fixing online manual mode so it doesn't reset in progress tasks by making a fake pilot entry</li> </ul&gt