25,085 research outputs found
P Systems with Randomized Right-hand Sides of Rules
P systems are a model of hierarchically compartmentalized multiset rewriting.
We introduce a novel kind of P systems in which rules are dynamically constructed
in each step by non-deterministic pairing of left-hand and right-hand sides. We de ne
three variants of right-hand side randomization and compare each of them with the power
of conventional P systems. It turns out that all three variants enable non-cooperative P
systems to generate exponential (and thus non-semi-linear) number languages. We also
give a binary normal form for one of the variants of P systems with randomized rule
right-hand sides. Finally, we also discuss extensions of the three variants to tissue P
systems, i.e., P systems on an arbitrary graph structure
Prototyping Formal System Models with Active Objects
We propose active object languages as a development tool for formal system
models of distributed systems. Additionally to a formalization based on a term
rewriting system, we use established Software Engineering concepts, including
software product lines and object orientation that come with extensive tool
support. We illustrate our modeling approach by prototyping a weak memory
model. The resulting executable model is modular and has clear interfaces
between communicating participants through object-oriented modeling.
Relaxations of the basic memory model are expressed as self-contained variants
of a software product line. As a modeling language we use the formal active
object language ABS which comes with an extensive tool set. This permits rapid
formalization of core ideas, early validity checks in terms of formal invariant
proofs, and debugging support by executing test runs. Hence, our approach
supports the prototyping of formal system models with early feedback.Comment: In Proceedings ICE 2018, arXiv:1810.0205
Decreasing Diagrams and Relative Termination
In this paper we use the decreasing diagrams technique to show that a
left-linear term rewrite system R is confluent if all its critical pairs are
joinable and the critical pair steps are relatively terminating with respect to
R. We further show how to encode the rule-labeling heuristic for decreasing
diagrams as a satisfiability problem. Experimental data for both methods are
presented.Comment: v3: missing references adde
An interactive two-dimensional approach to query aspects rewriting in systematic reviews. IMS unipd at CLEF eHealth task 2
International audienc
Minimization Strategies for Maximally Parallel Multiset Rewriting Systems
Maximally parallel multiset rewriting systems (MPMRS) give a convenient way
to express relations between unstructured objects. The functioning of various
computational devices may be expressed in terms of MPMRS (e.g., register
machines and many variants of P systems). In particular, this means that MPMRS
are computationally complete; however, a direct translation leads to quite a
big number of rules. Like for other classes of computationally complete
devices, there is a challenge to find a universal system having the smallest
number of rules. In this article we present different rule minimization
strategies for MPMRS based on encodings and structural transformations. We
apply these strategies to the translation of a small universal register machine
(Korec, 1996) and we show that there exists a universal MPMRS with 23 rules.
Since MPMRS are identical to a restricted variant of P systems with antiport
rules, the results we obtained improve previously known results on the number
of rules for those systems.Comment: This article is an improved version of [1
Analytical learning and term-rewriting systems
Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques
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Automated verification of refinement laws
Demonic refinement algebras are variants of Kleene algebras. Introduced by von Wright as a light-weight variant of the refinement calculus, their intended semantics are positively disjunctive predicate transformers, and their calculus is entirely within first-order equational logic. So, for the first time, off-the-shelf automated theorem proving (ATP) becomes available for refinement proofs. We used ATP to verify a toolkit of basic refinement laws. Based on this toolkit, we then verified two classical complex refinement laws for action systems by ATP: a data refinement law and Back's atomicity refinement law. We also present a refinement law for infinite loops that has been discovered through automated analysis. Our proof experiments not only demonstrate that refinement can effectively be automated, they also compare eleven different ATP systems and suggest that program verification with variants of Kleene algebras yields interesting theorem proving benchmarks. Finally, we apply hypothesis learning techniques that seem indispensable for automating more complex proofs
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