In traditional approach, planning and scheduling are two separate and successive operations. The process planning phase is mostly about the physical aspect of planning, where a product range, production quantities, machines, tools, material and accessories are selected. The emphasis in the second, scheduling phase, is on the time aspect. The plan is the basis to determine the sequence of operat...

By looping over a set of behaviors, reactive systems use repetition and feedback to deal with errors and environmental uncertainty. Their robust, fault-tolerant performance makes reactive systems desirable for executing plans. However, most planning systems cannot reason about the loops that characterize reactive systems. In this paper, we show how the structured application of abstraction and ...

this research uses a comprehensive method to solve a combinatorial problem of distribution network expansion planning (dnep) problem. the proposed multi-objective scheme aims to improve power system's accountability and system performance parameters, simultaneously, in the lowest possible costs. the dynamic programming approach is implemented in order to find the optimal sizing, siting and...

In this paper, we describe a reactive robot architecture that uses fast re-planning methods to avoid the shortcomings of reactive navigation, such as getting stuck in box canyons or in front of small openings. Our robot architecture differs from others in that it gives planning progressively greater control of the robot if reactive navigation continues to fail, until planning controls the robot...

Planning has played an important role in many relevant areas of AI. The classical planning problem consists of the following task: Given a state of the world, several actions and their (deterministic) effects, find a sequence of actions (viz. a plan) to reach a certain goal state. In recent years, several successful logic-based approaches to classical planning have been proposed (cf. [33, 11, 6...

We propose a multi-agent vision-based architecture to solve complex sensor-based planning tasks. A test bed implementation, with skills such as vision and collision avoidance, was used to run experiments in the proposed architecture. We demonstrate experimentally how the system can execute successfully complex assembly plans while dealing with unpredictable events and imprecise information, wit...

Intelligent agents are powerful tools for complex and dynamic problems. Belief Desire Intension (BDI) is one of the most popular agent architectures for reactive goal directed agents. Planning is intrinsic for intelligent behaviour. But planning from first principle is costly in terms of computation time and resources. BDI agents retain their reactive property by avoiding planning from real-tim...