Differently from results highlight the strong performances of our planner, the classical case, where plans are sequences of instantaneous which achieved the highest coverage (i.e., number of solved actions and variables are Boolean, in these problems problems) in 9 out of 10 domains, while the second-best actions may have a duration, are executed concurrently over planner had the highest coverage in 4 domains. Additionally, time, and can affect Boolean and numeric variables at both compared to the other symbolic planners, our system is able the start and end of their execution. These two extensions to find a valid plan with a lower bound on all the problems.

In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem $\Pi$, a bound $n$ and a pattern -- defined as an arbitrary sequence of actions -- we encode the problem of finding a plan for $\Pi$ with bound $n$ as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-$\exists$ encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems -- including the ones which participated in this year's International Planning Competition (IPC) -- and we show that our planner Patty has remarkably good comparative performances on this year's IPC problems.

This paper continues the line of work on representing properties of actions in nonmonotonic formalisms that stresses the distinction between being "true" and being "caused", as in the system of causal logic introduced by McCain and Turner and in the action language C proposed by Giunchiglia and Lifschitz. The only fluents directly representable in language C+ are truth-valued fluents, which is often inconvenient. We show that both causal logic and language C can be extended to allow values from arbitrary nonempty sets. Our extension of language C, called C+, also makes it possible to describe actions in terms of their attributes, which is important from the perspective of elaboration tolerance. We describe an embedding of C+ in causal theories with multi-valued constants, relate C+ to Pednault's action language ADL, and show how multi-valued constants can be eliminated in favor of Boolean constants.

Business processes under authorization control are sets of coordinated activities subject to a security policy stating which agent can access which resource. Their behavior is difficult to predict due to the complex and unexpected interleaving of different execution flows within the process. Therefore, serious flaws may go undetected and manifest themselves only after deployment. This problem may be tackled by applying formal methods to reason about business process models. In this paper we outline the main contributions in this application domain of (Armando et al. 2012), that uses the action-based planning language C and the Causal Calculator tool CCalc. C is used to specify a business process from the banking domain that is representative of an important class of business processes of practical relevance, and proved to be a rich and natural formal specification language in this domain. CCalc is then used to automatically solve three reasoning tasks that arise in this context. We also compare C with the SMV specification language used in model-checking: the comparison highlights some key advantages of C in the business process domain.

The 2003International Conference on Automated Planning and Scheduling (ICAPS-03) was held 9 to 13 June 2003 in Trento, Italy. It was chaired by Enrico Giunchiglia (University of Genova), Nicola Muscettola (NASA Ames), and Dana Nau (University of Maryland). Piergiorgio Bertoli and Marco Benedetti (both from ITC-IRST) were the local chair and the workshop-tutorial coordination chair, respectively.

The 2003International Conference on Automated Planning and Scheduling (ICAPS-03) was held 9 to 13 June 2003 in Trento, Italy. It was chaired by Enrico Giunchiglia (University of Genova), Nicola Muscettola (NASA Ames), and Dana Nau (University of Maryland). Piergiorgio Bertoli and Marco Benedetti (both from ITC-IRST) were the local chair and the workshop-tutorial coordination chair, respectively.