A great effort has been made today in the area of Artificial Intelligence for defining reliable automated planning systems that can be applied in real life applications. That leads to the need of a systematic design process, in which the initial phases are not neglected and where Knowledge and Requirement Engineering tools have a fundamental role for supporting designers. Following this principle, this paper presents the evolution of the tool itSIMPLE which implements a KE integrated environment where designers can perform knowledge acquisition, domain modeling, domain model analysis, model testing, maintenance and plan analysis processes by using different well-known languages such as UML, Petri Nets, PDDL and XML, each one of them with its best contribution. The tool supports users in an organized object-oriented domain design process with a friendly and easy-to-use interface.
Schramm, Joachim (Clausthal University of Technology) | Strickroth, Sven (Clausthal University of Technology) | Le, Nguyen-Thinh (Clausthal University of Technology) | Pinkwart, Niels (Clausthal University of Technology)
Modeling skills are essential during the process of learning programming. ITS systems for modeling are typically hard to build due to the ill-definedness of most modeling tasks. This paper presents a system that can teach UML skills to novice programmers. The system is “simple and cheap” in the sense that it only requires an expert solution against which the student solutions are compared, but still flexible enough to accommodate certain degrees of solution flexibility and variability that are characteristic of modeling tasks. An empirical evaluation via a controlled lab study showed that the system worked fine and, while not leading to significant learning gains as compared to a control condition, still revealed some promising results.
Virtual Enterprise is an important organization pattern for future enterprises, one of whose major functions is the distributed and parallel business process execution. This paper aims at the study on business process modeling in virtual enterprises. Based on the object-oriented description of business processes in virtual enterprises, we propose a UML and Petri nets integrated modeling method for business processes in virtual enterprises. The method provides an integrative framework supporting requirement description, model specification and design, model analysis and simulation, and model implementation.
Dynamic programming algorithms have been successfully applied to propositional stochastic planning problems by using compact representations, in particular algebraic decision diagrams, to capture domain dynamics and value functions. Work on symbolic dynamic programming lifted these ideas to first order logic using several representation schemes. Recent work introduced a first order variant of decision diagrams (FODD) and developed a value iteration algorithm for this representation. This paper develops several improvements to the FODD algorithm that make the approach practical. These include, new reduction operators that decrease the size of the representation, several speedup techniques, and techniques for value approximation. Incorporating these, the paper presents a planning system, FODD-Planner, for solving relational stochastic planning problems. The system is evaluated on several domains, including problems from the recent international planning competition, and shows competitive performance with top ranking systems. This is the first demonstration of feasibility of this approach and it shows that abstraction through compact representation is a promising approach to stochastic planning.
We investigate the use of relaxed decision diagrams (DDs) for computing admissible heuristics for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of two novel DD encodings for a DFP task: a multivalued decision diagram that includes the sequencing aspect of the problem and a binary decision diagram representation of its sequential relaxation. We present construction algorithms for each DD that leverage these different perspectives of the DFP task and provide theoretical and empirical analyses of the associated heuristics. We further show that relaxed DDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while DD-based heuristics trail the state of the art, even small relaxed DDs are competitive with the linear programming heuristic for the DFP task, thus, revealing novel ways of designing admissible heuristics.