What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

09501 Abstracts Collection -- Software Synthesis

From 06.12.09 to 11.12.09, the Dagstuhl Seminar 09501 ``Software Synthesis \u27\u27 in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

Model compilation: An approach to automated model derivation

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration

Planning, Acting, and Learning in Incomplete Domains

The engineering of complete planning domain descriptions is often very costly because of human error or lack of domain knowledge. Learning complete domain descriptions is also very challenging because many features are irrelevant to achieving the goals and data may be scarce. Given incomplete knowledge of their actions, agents can ignore the incompleteness, plan around it, ask questions of a domain expert, or learn through trial and error. Our agent Goalie learns about the preconditions and effects of its incompletely-specified actions by monitoring the environment state. In conjunction with the plan failure explanations generated by its planner DeFault, Goalie diagnoses past and future action failures. DeFault computes failure explanations for each action and state in the plan and counts the number of incomplete domain interpretations wherein failure will occur. The questionasking strategies employed by our extended Goalie agent using these conjunctive normal form-based plan failure explanations are goal-directed and attempt to approach always successful execution while asking the fewest questions possible. In sum, Goalie: i) interleaves acting, planning, and question-asking; ii) synthesizes plans that avoid execution failure due to ignorance of the domain model; iii) uses these plans to identify relevant (goal-directed) questions; iv) passively learns about the domain model during execution to improve later replanning attempts; v) and employs various targeted (goal-directed) strategies to ask questions (actively learn). Our planner DeFault is the first reason about a domain\u27s incompleteness to avoid potential plan failure. We show that DeFault performs best by counting prime implicants (failure diagnoses) rather than propositional models. Further, we show that by reasoning about incompleteness in planning (as opposed to ignoring it), Goalie fails and replans less often, and executes fewer actions. Finally, we show that goal-directed knowledge acquisition - prioritizing questions based on plan failure diagnoses - leads to fewer questions, lower overall planning and replanning time, and higher success rates than approaches that naively ask many questions or learn by trial and error

The impact of resources on decision making

Decision making is a significant activity within industry and although much attention has been paid to the manner in which goals impact on how decision making is executed, there has been less focus on the impact decision making resources can have. This article describes an experiment that sought to provide greater insight into the impact that resources can have on how decision making is executed. Investigated variables included the experience levels of decision makers and the quality and availability of information resources. The experiment provided insights into the variety of impacts that resources can have upon decision making, manifested through the evolution of the approaches, methods, and processes used within it. The findings illustrated that there could be an impact on the decision-making process but not on the method or approach, the method and process but not the approach, or the approach, method, and process. In addition, resources were observed to have multiple impacts, which can emerge in different timescales. Given these findings, research is suggested into the development of resource-impact models that would describe the relationships existing between the decision-making activity and resources, together with the development of techniques for reasoning using these models. This would enhance the development of systems that could offer improved levels of decision support through managing the impact of resources on decision making