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

Fully Observable Non-deterministic Planning as Assumption-Based Reactive Synthesis

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard “fairness” assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special “structural” properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.Fil: D'ippolito, Nicolás Roque. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Rodriguez, Natalia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Sardina, Sebastian. RMIT University; Australi

SmartPM: Automated Adaptation of Dynamic Processes

In this demonstration paper, we present the first working version of SmartPM, a Process Management System that is able to automatically adapt dynamic processes at run-time when unanticipated exceptions occur, thus requiring no specification of recovery policies at design-time

Parallel behavior composition for manufacturing

A key problem in the manufacture of highlycustomized products is the synthesis of controllers able to manufacture any instance of a given product type on a given production or assembly line. In this paper, we extend classical AI behavior composition to manufacturing settings. We first introduce a novel solution concept for manufacturing composition, target production processes, that are able to manufacture multiple instances of a product simultaneously in a given production plant. We then propose a technique for synthesizing the largest target production process, together with an associated controller for the machines in the plant

handling, declarative goals, and planning

A BDI agent programming language with failur

HTN-Like Solutions for Classical Planning Problems: An Application to BDI Agent Systems

In this paper we explore the question of what characterises a desirable plan of action and how such a plan could be computed, in the context of systems that already possess a certain amount of hierarchical domain knowledge. In contrast to past work in this setting, which focuses on generating low-level plans, losing much of the domain knowledge inherent in such systems, we argue that plans ought to be HTN-like or abstract, i.e., re-use and respect the user-supplied know-how in the underlying domain. In doing so, we recognise an intrinsic tension between striving for abstract plans but ensuring that unnecessary actions, not linked to the specific goal to be achieved, are avoided. We explore this tension by characterising the set of “ideal” abstract plans that are non-redundant but maximally abstract, and then develop a more limited yet feasible account in which a given (arbitrary) abstract plan is “specialised” into one such non-redundant plan that is as abstract as possible. We present an algorithm that can compute such specialisations, and analyse the theoretical properties of our proposal

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

Knowledge-Intensive Business Processes: Proceedings of the 1st International Workshop on Knowledge-intensive Business Processes

Nowadays, Workflow Management Systems (WfMSs) and, more generally, Process Management Systems (PMPs) are process-aware Information Systems (PAISs), are widely used to support many human organizational activities, ranging from well-understood, relatively stable and structures processes (supply chain management, postal delivery tracking, etc.) to processes that are more complicated, less structured and may exhibit a high degree of variation (health-care, emergency management, etc.). Every aspect of a business process involves a certain amount of knowledge which may be complex depending on the domain of interest. The adequate representation of this knowledge is determined by the modeling language used. Some processes behave in a way that is well understood, predictable and repeatable: the tasks are clearly delineated and the control flow is straightforward. Recent discussions, however, illustrate the increasing demand for solutions for knowledge-intensive processes, where these characteristics are less applicable. The actors involved in the conduct of a knowledge-intensive process have to deal with a high degree of uncertainty. Tasks may be hard to perform and the order in which they need to be performed may be highly variable. Modeling knowledge-intensive processes can be complex as it may be hard to capture at design-time what knowledge is available at run-time. In realistic environments, for example, actors lack important knowledge at execution time or this knowledge can become obsolete as the process progresses. Even if each actor (at some point) has perfect knowledge of the world, it may not be certain of its beliefs at later points in time, since tasks by other actors may change the world without those changes being perceived. Typically, a knowledge-intensive process cannot be adequately modeled by classical, state of the art process/workflow modeling approaches. In some respect there is a lack of maturity when it comes to capturing the semantic aspects involved, both in terms of reasoning about them. The main focus of the 1st International Workshop on Knowledge-intensive Business processes (KiBP 2012) was investigating how techniques from different fields, such as Artificial Intelligence (AI), Knowledge Representation (KR), Business Process Management (BPM), Service Oriented Computing (SOC), etc., can be combined with the aim of improving the modeling and the enactment phases of a knowledge-intensive process. The 1st International Workshop on Knowledge-intensive Business process (KiBP 2012) was held as part of the program of the 2012 Knowledge Representation & Reasoning International Conference (KR 2012) in Rome, Italy, in June 2012. The workshop was hosted by the Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio Ruberti of Sapienza Universita di Roma, with financial support of the University, through grant 2010-C26A107CN9 TESTMED, and the EU Commission through the projects FP7-25888 Greener Buildings and FP7-257899 Smart Vortex. This volume contains the 5 papers accepted and presented at the workshop. Each paper was reviewed by three members of the internationally renowned Program Committee. In addition, a further paper was invted for inclusion in the workshop proceedings and for presentation at the workshop. There were two keynote talks, one by Marlon Dumas (Institute of Computer Science, University of Tartu, Estonia) on "Integrated Data and Process Management: Finally?" and the other by Yves Lesperance (Department of Computer Science and Engineering, York University, Canada) on "A Logic-Based Approach to Business Processes Customization" completed the scientific program. We would like to thank all the Program Committee members for the valuable work in selecting the papers, Andrea Marrella for his valuable work as publication and publicity chair of the workshop, and Carola Aiello and the consulting agency Consulta Umbria for the organization of this successful event