sion diagrams (BDDs) to compactly represent world states in a planning problem and efficiently explore the underlying state space. It was the first general planning system based on model-checking methods. It can handle the STRIPS subset of the PDDL language and some additional features from ADL, namely, negative preconditions and (universal) conditional effects. At the Fifth International Confe...

Planning has been at the forefront of research in the areas of Artificial Intelligence and cognitive science. High-level action description languages (e.g., language A) have been used to specify, verify and diagnose plans. Timed Planning is planning under real-time constraints. To specify timed planning problems, an extension of the action description language A with real-time stopwatches, call...

The technology of artificial intelligence (AI) planning is being adopted across many different disciplines. This has resulted in the wider use of the Planning Domain Definition Language (PDDL), where it is being used to model planning problems of different natures. One such area where AI planning is particularly attractive is engineering, where the optimisation problems are mathematically rich....

A new approach to automated process planning, integrating generative and case-based planning methods, is presented. Unlike most of the existing Artificial Intelligence (AI) based process planning systems, this approach makes use of a dynamic knowledge base containing old cases, i.e. part descriptions and corresponding process plans. This knowledge base grows with experience and therefore may re...

The central theme of this research is simple: people are able to cope with complexities of the real world because of their ability to improvise, which makes them more flexible and adaptable. Most artificial intelligence systems are rigid, do not scaleup to challenges of the real world, and do not exhibit improvisational behavior. If we acknowledge the benefits of improvisation methods for peopl...

Markov decision processes (MDPs) are applied as a standard model in Artificial Intelligence planning. MDPs are used to construct optimal or near optimal policies or plans. One area that is often missing from discussions of planning under stochastic environment is how MDPs handle safety constraints expressed as probability of reaching threat states. We introduce a method for finding a value opti...

This paper presents the concepts of the intelligent system for aiding of the module assembly technology. The first part of this paper presents a project of intelligent support system for computer aided assembly process planning. The second part includes a coincidence description of the chosen aspects of implementation of this intelligent system using technologies of artificial intelligence (art...

In recent times, improvements in imaging technology have made available an incredible array of dormation in image format. While powerful and sophisticated image processing software tools are available to prepare and analyze the data, these tools are complex and cumbersome, requiring significant expertise to properly operate. Thus, in order to extract (e.g., mine or analyze) useful information f...

For an embedded real-time process-control system incorporating artificial-intelligence programs, the system reliability is determined by both the software-driven response computation time and the hardware-driven response execution time. A general model, based on the probability that the system can accomplish its mission under a time constraint without incurring failure, is proposed to estimate ...

The relevance of computer science to economic planning is defended. An algorithm for constructing a balanced economic plan is presented and found to be of time order Nlog(N) in the complexity of the economy. The time taken to perform a complete plan optimization in natural units for a whole economy is estimated. Plans and computational costs This is a paper by a computer scientist that disputes...