Efficiently Integrating Boolean Reasoning and Mathematical Solving

Many real-world problems require the ability of reasoning efficiently on formulae which are boolean combinations of boolean and unquantified mathematical propositions. This task requires a fruitful combination of efficient boolean reasoning and mathematical solving capabilities. SAT tools and mathematical reasoners are respectively very effective on one of these activities each, but not on both. In this paper we present a formal framework, a generalized algorithm and architecture for integrating boolean reasoners and mathematical solvers so that they can efficiently solve boolean combinations of boolean and unquantified mathematical propositions. We describe many techniques to optimize this integration, and highlight the main requirements for SAT tools and mathematicalsolvers to maximize the benefits of their integration

AUTOMATED COMPOSITION OF WEB SERVICES VIA PLANNING IN ASYNCHRONOUS DOMAINS\ud

The service-oriented paradigm promises a novel degree of interoperability between\ud business processes, and is leading to a major shift in way distributed applications are\ud designed and realized. While novel and more powerful services can be obtained, in such\ud setting, by suitably orchestrating existing ones, manually developing such orchestrations\ud is highly demanding, time-consuming and error-prone. Providing automated service\ud composition tools is therefore essential to reduce the time to market of services, and\ud ultimately to successfully enact the service-oriented approach.\ud In this paper, we show that such tools can be realized based on the adoption and extension\ud of powerful AI planning techniques, taking the “planning via model-checking” approach\ud as a stepping stone. In this respect, this paper summarizes and substantially extends a\ud research line that started early in this decade and has continued till now. Specifically, this\ud work provides three key contributions.\ud First, we describe a novel planning framework for the automated composition of Web\ud services, which can handle services specified and implemented using industrial standard\ud languages for business processes modeling and execution, like ws-bpel. Since these\ud languages describe stateful Web services that rely on asynchronous communication\ud primitives, a distinctive aspect of the presented framework is its ability to model and\ud solve planning problems for asynchronous domains.\ud Second, we formally spell out the theory underlying the framework, and provide algorithms\ud to solve service composition in such framework, proving their correctness and\ud completeness. The presented algorithms significantly extend state-of-the-art techniques\ud for planning under uncertainty, by allowing the combination of asynchronous domains\ud according to behavioral requirements.\ud Third, we provide and discuss an implementation of the approach, and report extensive\ud experimental results which demonstrate its ability to scale up to significant cases for\ud which the manual development of ws-bpel composed services is far from trivial and time\ud 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

Planning with Extended Goals and Partial Observability

Planning in nondeterministic domains with temporally extended goals under partial observability is one of the most challenging problems in planning. Simpler subsets of this problem have been already addressed in the literature, but the general combination of extended goals and partial observability is, to the best of our knowledge, still an open problem. In this paper we present a first attempt to solve the problem, namely, we define an algorithm that builds plans in the general setting of planning with extended goals and partial observability. The algorithm builds on the top of the techniques developed in the planning with model checking framework for the restricted problems of extended goals and of partial observabilit

Design Verification of a Safety-Critical Embedded Verifier

This case study shows the use of ACL2 for the design verification of a selected piece of safety-critical software, called an Embedded Verifier. The Embedded Verifier chacks on-line that each execution of a safety-critical translator is correct. This study originates from an industrial partnership projec

Planning with Extended Goals and Partial Observability

Planning in nondeterministic domains with temporally extended goals under partial observability is one of the most challenging problems in planning. Simpler subsets of this problem have been already addressed in the literature, but the general combination of extended goals and partial observability is, to the best of our knowledge, still an open problem. In this paper we present a first attempt to solve the problem, namely, we define an algorithm that builds plans in the general setting of planning with extended goals and partial observability. The algorithm builds on the top of the techniques developed in the planning with model checking framework for the restricted problems of extended goals and of partial observability