An Introduction to Simulation-Based Techniques for Automated Service Composition

This work is an introduction to the author's contributions to the SOC area, resulting from his PhD research activity. It focuses on the problem of automatically composing a desired service, given a set of available ones and a target specification. As for description, services are represented as finite-state transition systems, so to provide an abstract account of their behavior, seen as the set of possible conversations with external clients. In addition, the presence of a finite shared memory is considered, that services can interact with and which provides a basic form of communication. Rather than describing technical details, we offer an informal overview of the whole work, and refer the reader to the original papers, referenced throughout this work, for all details

Supervisory Control for Behavior Composition

We relate behavior composition, a synthesis task studied in AI, to supervisory control theory from the discrete event systems field. In particular, we show that realizing (i.e., implementing) a target behavior module (e.g., a house surveillance system) by suitably coordinating a collection of available behaviors (e.g., automatic blinds, doors, lights, cameras, etc.) amounts to imposing a supervisor onto a special discrete event system. Such a link allows us to leverage on the solid foundations and extensive work on discrete event systems, including borrowing tools and ideas from that field. As evidence of that we show how simple it is to introduce preferences in the mapped framework

Generalized Planning with Loops under Strong Fairness Constraints

Abstract We consider a generalized form of planning, possibly involving loops, that arises in nondeterministic domains when explicit strong fairness constraints are asserted over the planning domain. Such constraints allow us to specify the necessity of occurrence of selected effects of nondeterministic actions over domain's runs. Also they are particularly meaningful from the technical point of view because they exhibit the expressiveness advantage of LTL over CTL in verification. We show that planning for reachability and maintenance goals is EXPTIME-complete in this setting, that is, it has the same complexity as conditional planning in nondeterministic domains (without strong fairness constraints). We also show that within the EXPTIME bound one can solve the more general problems of realizing agent planning programs as well as composition-based planning in the presence of strong fairness constraints

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

Behavior composition optimisation

The behavior composition problem involves automatic synthesis of a controller that is able to “realize” (i.e., implement) a desired target specification by suitably controlling a collection of already available, partially controllable, behaviors running in a partially predictable shared environment. A behavior in our context refers to an already existing functionality such as the logic of a device, a service, a standalone component, etc; whereas a target specification represents the desired non-existent functionality that is meant to be obtained through the available behaviors. Previous work in behavior composition has exclusively aimed at synthesising exact controllers, those that bring about the desired specification completely. One open issue has resisted principled solutions: if the target specification cannot be completely implemented, is there a way to realize it “optimally”? In this doctoral thesis, we propose qualitative and quantitative optimisation frameworks that are able to accommodate composition problems that do not admit the “perfect” coordinating controller. In the qualitative setting, we rely on the formal notion of simulation to define realizable fragments of a target specification and show the existence of a unique supremal realizable fragment for a given problem instance. In addition, we extend the qualitative framework by introducing exogenous uncontrollable events to represent observable contingencies. In the quantitative setting, we provide a decision theoretic approach to behavior composition by quantifying the uncertainties present in the domain. In all cases, we provide effective techniques to compute optimal solutions and study their computational properties

Automatic Synthesis of New Behaviors from a Library of Available Behaviors

We consider the problem of synthesizing a fully controllable target behavior from a set of available partially controllable behaviors that are to execute within a shared partially predictable, but fully observable, environment. Behaviors are represented with a sort of nondeterministic transition systems, whose transitions are conditioned on the current state of the environment, also represented as a nondeterministic finite transition system. On the other hand, the target behavior is assumed to be fully deterministic and stands for the behavior that the system as a whole needs to guarantee. We formally define the problem within an abstract framework, characterize its computational complexity, and propose a solution by appealing to satisfiability in Propositional Dynamic Logic, which is indeed optimal with respect to computational complexity. We claim that this problem, while novel to the best of our knowledge, can be instantiated to multiple specific settings in different contexts and can thus be linked to different research areas of AI, including agent-oriented programming and cognitive robotics, control, multi-agent coordination, plan integration, and automatic web-service composition.