Abstract. Tabling is an implementation technique that solves some limitations of Prolog’s operational semantics in dealing with recursion and redundant sub-computations. Arguably, the SLDT and DRA strategies are the two most successful extensions to standard linear tabled evaluation. In this work

This paper investigates the optimization by fold/unfold of functional-logic programs with operational semantics based on needed narrowing. Transformation sequences are guided by the tupling strategy, a powerful method that avoids multiple accesses to data structures and redundant sub-computations

terminates more often than Prolog (e.g. for all datalog programs), avoids redundant sub-computations, and computes the well-founded model of normal logic programs.

Tabling is an implementation technique that solves some limitations of Prolog’s operational se-mantics in dealing with recursion and redundant sub-computations. Tabling works by memorizing generated answers and then by reusing them on similar calls that appear during the resolution process. In a

for checking for subsumption relationships between two feature structures; a special case of this gives a fast equality test. The subsumption check is used in a parser (described elsewhere in this volume) which "packs" local ambiguities to avoid performing redundant sub-computations.

Abstract. Tabled evaluation is a recognized and powerful implementation technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant sub-computations. Tabling based systems use call similarity to determine if a tabled subgoal will produce their own

Virtual machine monitors (VMMs) are a popular platform for Internet hosting centers and cloud-based compute services. By multiplexing hardware resources among virtual machines (VMs) running commodity operating systems, VMMs decrease both the capital outlay and management overhead of hosting centers

The problem of optimally approximating a function with a linear expansion over a redundant dictionary of waveforms is NP-hard. The greedy matching pursuit algorithm and its orthogonalized variant produce sub-optimal function expansions by iteratively choosing dictionary waveforms that best match

network of Γ is a subset of Γ which contains no redundant constraints but has the same solution set as Γ. It is natural to ask how to compute a prime network, and when it is unique. In this paper, we show that this problem is in general co-NP hard, but becomes tractable if Γ is over a tractable sub

Abstract: This study deals with a compound standby redundant system consisting of three sub-systems A, B, and C connected in series. The sub-system B consists of one main unit and the other is its standby redundant unit. These units further consist of two sub-units connected in series. The sub