Piergiorgio Bertoli

Automated composition of Web services via planning in asynchronous domains

We propose a novel planning framework for the automated composition of web services. We consider services that are specified and implemented in industrial standard languages for business processes modeling and execution, like BPEL4WS. These languages describe web services whose behavior is intrinsically asynchronous. For this reason, the key aspect of our framework is the modeling of asynchronous planning problems. In the paper we describe the framework and propose a planning approach that is based on state of the art techniques for planning under uncertainty. Our experiments show that this approach can scale up to significant cases, i.e., to cases in which the manual development of BPEL4WS composed services is not trivial and is time consuming.

Planning and Monitoring Web Service Composition

The ability to automatically compose web services, and to monitor their execution, is an essential step to substantially decrease time and costs in the development, integration, and maintenance of complex services. In this paper, we exploit techniques based on the “Planning as Model Checking” approach to automatically compose web services and synthesize monitoring components. By relying on such a flexible technology, we are able to deal with the difficulties stemming from the unpredictability of external partner services, the opaqueness of their internal status, and the presence of complex behavioral requirements. We test our approach on a simple, yet realistic example; the results provide a witness to the potentiality of this approach.

Automated synthesis of composite BPEL4WS Web services

In this paper we propose a technique for the automated synthesis of new composite Web services. Given a set of abstract BPEL4WS descriptions of component services, and a composition requirement, we automatically generate an executable BPEL4WS process that, once deployed, is able to interact with the components to satisfy the requirement. We implement the proposed approach exploiting efficient synthesis techniques, and experiment with some case studies taken from real world applications and with a parameterized domain. We show that the technique can scale up to cases in, which the manual development of BPEL4WS composite services is not trivial and is time consuming.

A SAT Based Approach for Solving Formulas over Boolean and Linear Mathematical Propositions

The availability of decision procedures for combinations of boolean and linear mathematical propositions opens the ability to solve problems arising from real-world domains such as verification of timed systems and planning with resources. In this paper we present a general and efficient approach to the problem, based on two main ingredients. The first is a DPLL-based SAT procedure, for dealing efficiently with the propositional component of the problem. The second is a tight integration, within the DPLL architecture, of a set of mathematical deciders for theories of increasing expressive power. A preliminary experimental evaluation shows the potential of the approach.

Strong planning under partial observability

Rarely planning domains are fully observable. For this reason, the ability to deal with partial observability is one of the most important challenges in planning. In this paper, we tackle the problem of strong planning under partial observability in nondeterministic domains: find a conditional plan that will result in a successful state, regardless of multiple initial states, nondeterministic action effects, and partial observability.We make the following contributions. First, we formally define the problem of strong planning within a general framework for modeling partially observable planning domains. Second, we propose an effective planning algorithm, based on and-or search in the space of beliefs. We prove that our algorithm always terminates, and is correct and complete. In order to achieve additional effectiveness, we leverage on a symbolic, BDD-based representation for the domain, and propose several search strategies. We provide a thorough experimental evaluation of our approach, based on a wide selection of benchmarks. We compare the performance of the proposed search strategies, and identify a uniform winner that combines heuristic distance measures with mechanisms that reduce runtime uncertainty. Then, we compare our planner MBP with other state-of-the art-systems. MBP is able to outperform its competitor systems, often by orders of magnitude.

ASTRO: supporting composition and execution of web services

Web services are rapidly emerging as the reference paradigm for the interaction and coordination of distributed business processes. In several research papers we have shown how advanced automated planning techniques can be exploited to automatically compose web services, and to synthesize monitoring components that control their execution. In this demo we show how these techniques have been implemented in the ASTRO toolset (http://www.astroproject.org), a set of tools that extend existing platforms for web service design and execution with automated composition and execution monitoring functionalities.

Specification and Integration of Theorem Provers and Computer Algebra Systems

Computer algebra systems (CASs) and automated theorem provers (ATPs) exhibit complementary abilities. CASs focus on efficiently solving domain-specific problems. ATPs are designed to allow for the formalization and solution of wide classes of problems within some logical framework. Integrating CASs and ATPs allows for the solution of problems of a higher complexity than those confronted by each class alone. However, most experiments conducted so far followed an ad-hoc approach, resulting in tailored solutions to specific problems. A structured and principled approach is necessary to allow for the sound integration of systems in a modular way. The Open Mechanized Reasoning Systems (OMRS) framework was introduced for the specification and implementation of mechanized reasoning systems, e.g. ATPs. The approach was recasted to the domain of computer algebra systems. In this paper, we introduce a generalization of OMRS, named OMSCS (Open Mechanized Symbolic Computation Systems). We show how OMSCS can be used to soundly express CASs, ATPs, and their integration, by formalizing a combination between the Isabelle prover and the Maple algebra system. We show how the integrated system solves a problem which could not be tackled by each single system alone.

Integrating Discovery and Automated Composition: from Semantic Requirements to Executable Code

Web services are conveniently advertised and published based on (stateless) functional descriptions, while they are usually realized as (stateful) processes. Therefore, the automated enactment of complex Web services on the basis of pre-existing ones requires the ability to handle services described at very different abstraction levels. This is the main reason behind the current lack of approaches capable to perform automated end-to-end composition, starting from semantic requirements to obtain executable orchestrations of stateful processes. In this paper we achieve such a challenging goal, by modularly integrating a range of incrementally more complex techniques that cover the necessary discovery and composition phases. By gradually bridging the gap between the high-level requirements and the concrete realization of services, our architecture manages sensibly the complexity of the problem: incrementally more complex techniques are provided with incrementally more focused input. The tests of our architecture on a deployed scenario witness the functionality of the platform and its integrability with standard service engines.

Automated synthesis of executable web service compositions from BPEL4WS processes

We propose a technique for the automated synthesis of new composite web services. Given a set of abstract bpel4ws descriptions of component services, and a composition requirement, we automatically generate a concrete bpel4ws process that, when executed, interacts with the components and satisfies the requirement.We implement the proposed approach exploiting efficient representation techniques, and we show its scalability over case studies taken from a real world application and over a parameterized domain.

Integrating Boolean and Mathematical Solving: Foundations, Basic Algorithms, and Requirements

In the last years we have witnessed an impressive advance in the efficiency of boolean solving techniques, which has brought large previously intractable problems at the reach of state-of-the-art solvers. Unfortunately, simple boolean expressions are not expressive enough for representing many real-world problems, which require handling also integer or real values and operators. On the other hand, mathematical solvers, like computer-algebra systems or constraint solvers, cannot handle efficiently problems involving heavy boolean search, or do not handle them at all. In this paper we present the foundations and the basic algorithms for a new class of procedures for solving boolean combinations of mathematical propositions, which combine boolean and mathematical solvers, and we highlight the main requirements that boolean and mathematical solvers must fulfill in order to achieve the maximum benefits from their integration. Finally we show how existing systems are captured by our framework.