Backwards State-space Reduction for Planning in Dynamic Knowledge Bases

In this paper we address the problem of planning in rich domains, where knowledge representation is a key aspect for managing the complexity and size of the planning domain. We follow the approach of Description Logic (DL) based Dynamic Knowledge Bases, where a state of the world is represented concisely by a (possibly changing) ABox and a (fixed) TBox containing the axioms, and actions that allow to change the content of the ABox. The plan goal is given in terms of satisfaction of a DL query. In this paper we start from a traditional forward planning algorithm and we propose a much more efficient variant by combining backward and forward search. In particular, we propose a Backward State-space Reduction technique that consists in two phases: first, an Abstract Planning Graph P is created by using the Abstract Backward Planning Algorithm (ABP), then the abstract planning graph P is instantiated into a corresponding planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The advantage is that in the preliminary ABP phase we produce a symbolic plan that is a pattern to direct the search of the concrete plan. This can be seen as a kind of informed search where the preliminary backward phase is useful to discover properties of the state-space that can be used to direct the subsequent forward phase. We evaluate the effectiveness of our ABP+FPI algorithm in the reduction of the explored planning domain by comparing it to a standard forward planning algorithm and applying both of them to a concrete business case study.Comment: In Proceedings GRAPHITE 2014, arXiv:1407.767

Applying Mean-field Approximation to Continuous Time Markov Chains

The mean-field analysis technique is used to perform analysis of a systems with a large number of components to determine the emergent deterministic behaviour and how this behaviour modifies when its parameters are perturbed. The computer science performance modelling and analysis community has found the mean-field method useful for modelling large-scale computer and communication networks. Applying mean-field analysis from the computer science perspective requires the following major steps: (1) describing how the agents populations evolve by means of a system of differential equations, (2) finding the emergent deterministic behaviour of the system by solving such differential equations, and (3) analysing properties of this behaviour either by relying on simulation or by using logics. Depending on the system under analysis, performing these steps may become challenging. Often, modifications of the general idea are needed. In this tutorial we consider illustrating examples to discuss how the mean-field method is used in different application areas. Starting from the application of the classical technique, moving to cases where additional steps have to be used, such as systems with local communication. Finally we illustrate the application of the simulation and uid model checking analysis techniques

Folding Transformation Rules for Constraint Logic Programs

We consider the folding transformation rule for constraint logic programs. We propose an algorithm for applying the folding rule in the case where the constraints are linear equations and inequations over the rational or the real numbers. Basically, our algorithm consists in reducing a rule application to the solution of one or more systems of linear equations and inequations. We also introduce two variants of the folding transformation rule. The first variant combines the folding rule with the clause splitting rule, and the second variant eliminates the existential variables of a clause, that is, those variables which occur in the body of the clause and not in its head. Finally, we present the algorithms for applying these variants of the folding rule

Program Transformation for Development, Verification, and Synthesis of Software

In this paper we briefly describe the use of the program transformation methodology for the development of correct and efficient programs. We will consider, in particular, the case of the transformation and the development of constraint logic programs

Transformational Verification of Linear Temporal Logic

We present a new method for verifying Linear Temporal Logic (LTL) properties of finite state reactive systems based on logic programming and program transformation. We encode a finite state system and an LTL property which we want to verify as a logic program on infinite lists. Then we apply a verification method consisting of two steps. In the first step we transform the logic program that encodes the given system and the given property into a new program belonging to the class of the so-called linear monadic !-programs (which are stratified, linear recursive programs defining nullary predicates or unary predicates on infinite lists). This transformation is performed by applying rules that preserve correctness. In the second step we verify the property of interest by using suitable proof rules for linear monadic !-programs. These proof rules can be encoded as a logic program which always terminates, if evaluated by using tabled resolution. Although our method uses standard program transformation techniques, the computational complexity of the derived verification algorithm is essentially the same as the one of the Lichtenstein-Pnueli algorithm [9], which uses sophisticated ad-hoc techniques

Generalization Strategies for the Verification of Infinite State Systems

We present a method for the automated verification of temporal properties of infinite state systems. Our verification method is based on the specialization of constraint logic programs (CLP) and works in two phases: (1) in the first phase, a CLP specification of an infinite state system is specialized with respect to the initial state of the system and the temporal property to be verified, and (2) in the second phase, the specialized program is evaluated by using a bottom-up strategy. The effectiveness of the method strongly depends on the generalization strategy which is applied during the program specialization phase. We consider several generalization strategies obtained by combining techniques already known in the field of program analysis and program transformation, and we also introduce some new strategies. Then, through many verification experiments, we evaluate the effectiveness of the generalization strategies we have considered. Finally, we compare the implementation of our specialization-based verification method to other constraint-based model checking tools. The experimental results show that our method is competitive with the methods used by those other tools. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 24 pages, 2 figures, 5 table

Proving theorems by program transformation

In this paper we present an overview of the unfold/fold proof method, a method for proving theorems about programs, based on program transformation. As a metalanguage for specifying programs and program properties we adopt constraint logic programming (CLP), and we present a set of transformation rules (including the familiar unfolding and folding rules) which preserve the semantics of CLP programs. Then, we show how program transformation strategies can be used, similarly to theorem proving tactics, for guiding the application of the transformation rules and inferring the properties to be proved. We work out three examples: (i) the proof of predicate equivalences, applied to the verification of equality between CCS processes, (ii) the proof of first order formulas via an extension of the quantifier elimination method, and (iii) the proof of temporal properties of infinite state concurrent systems, by using a transformation strategy that performs program specialization

Program transformation for development, verification, and synthesis of programs

This paper briefly describes the use of the program transformation methodology for the development of correct and efficient programs. In particular, we will refer to the case of constraint logic programs and, through some examples, we will show how by program transformation, one can improve, synthesize, and verify programs

Stochastically timed predicate-based communication primitives for autonomic computing

Predicate-based communication allows components of a system to send messages and requests to ensembles of components that are determined at execution time through the evaluation of a predicate, in a multicast fashion. Predicate-based communication can greatly simplify the programming of autonomous and adaptive systems. We present a stochastically timed extension of the Software Component Ensemble Language (SCEL) that was introduced in previous work. Such an extension raises a number of non-trivial design and formal semantics issues with different options as possible solutions at different levels of abstraction. We discuss four of these options. We provide formal semantics and an illustration of the use of the language modeling a variant of a bike sharing system, together with some preliminary analysis of the system performance

A formalized framework for mobile cloud computing

Mobile Cloud Computing (MCC) is an emerging paradigm to transparently provide support for demanding tasks on resource-constrained mobile devices by relying on the integration with remote cloud services. Research in this field is tackling the multiple conceptual and technical challenges (e.g., how and when to offload) that are hindering the full realization of MCC. The Networked Autonomic Machine (NAM) framework is a tool that supports and facilitates the design networks of hardware and software autonomic entities, providing or consuming services or resources. Such a framework can be applied, in particular, to MCC scenarios. In this paper, we focus on NAM’s features related to the key aspects of MCC, in particular those concerning code mobility capabilities and autonomic offloading strategies. Our first contribution is the definition of a set of high-level actions supporting MCC. The second contribution is the proposal of a formal semantics for those actions, which provides the core NAM features with a precise formal characterization. Thus, the third contribution is the further development of the NAM conceptual framework and, in particular, the partial re-engineering of the related Java middleware. We show the effectiveness of the revised middleware by discussing the implementation of a Global Ambient Intelligence case study