Piergiorgio Bertoli

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Planning under partial observability is one of the most significant and challenging planning problems. It has been shown to be hard, both theoretically and experimentally. In this paper, we present a novel approach to the problem of planning under partial observability in non-deterministic domains. We propose an algorithm that searches through a (possibly(More)
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(More)
In this paper, we address the problem of the automated composition of web services by planning on their “knowledge level” models. We start from descriptions of web services in standard process modeling and execution languages, like BPEL4WS, and automatically translate them into a planning domain that models the interactions among services at the knowledge(More)
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(More)
This report has been submitted forr publication outside of ITC and will probably be copyrighted if accepted for publication. It has been issued as a Technical Report forr early dissemination of its contents. In view of the transfert of copy right too the outside publisher, its distribution outside of ITC priorr to publication should be limited to peer(More)
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(More)
The Model Based Planner (MBP) is a system for planning in non-deterministic domains. It can generate plans automatically to solve various planning problems, like conformant planning, planning under partial observability, and planning for temporally extended goals. Moreover, MBP can validate plans, and offers a variety of simulation functionalities for plans(More)
In this paper we tackle the problem of Conformant Planning: find a sequence of actions that guarantees goal achievement regardless of an uncertain initial condition and of nondeterministic action effects. Our approach, set in the framework of search in the belief space, is based on two main intuitions. First, symbolic model checking techniques, Binary(More)
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the problem of finding a sequential plan that guarantees the achievement of a goal regardless of the initial uncertainty and of nondeterministic action effects. In this paper, we present a new and efficient approach to conformant planning. The search paradigm,(More)