J. Britanik

Case-based process planning using an object-oriented model representation

Abstract This research focuses on the development of a process-planning system. This system utilizes case-based techniques for process selection and sequencing to combine the advantages of the variant and generative approaches to process planning. The case-based process planner utilizes an object-oriented model representation to operate on general three-dimensional prismatic parts, which are represented by a collection of features (e.g. slots, pockets, holes, etc.). Each feature subplan is developed by the case-based planner. Then the feature subplans are combined into the global process plan for the part via a hierarchical plan-merging mechanism. Abstracted feature subplans correspond to cases, which are used in subsequent planning operations to solve new problems. The abstracting and storing of feature subplans as cases is the primary mechanism by which the planner learns from its previous experiences to become more effective and efficient.

Case-based manufacturing process planning with integrated support for knowledge sharing

Computer aided process planning is a key part of bridging the link between design and manufacturing. Case-based reasoning provides an attractive paradigm for process planning as it combines the generative and variant approaches and improves the effectiveness of the planner by applying old experiences to solving new planning problems. We present a new case-based planning methodology that extends the capability and effectiveness of previous approaches by incorporating the ability to learn new domain knowledge from other planners via an integrated knowledge sharing mechanism. In addition, our planner has the capability to use multiple cases in the process of constructing a new plan, providing more effective utilization of the planners previous experiences. The planning algorithm is based on domain-independent partial-order planning, and provides a formal approach to case-based process planning.

Hierarchically merging plans in decomposable domains

Recent works in domain-independent plan merging have shown that the optimal plan merging problem is NP-hard, and heuristic algorithms have been proposed to generate near-optimal solutions. We have developed a plan merging methodology that merges partial-order plans based on the notion of plan fragments. In contrast to previous works, mergeability classes no longer necessarily form a partition over the set of actions in the input plans. This methodology applies to a class of planning domains which are decomposable. We also investigate the previously unexplored notion of alternative actions in domain-independent plan merging, which can improve the quality of the resulting merged plan, and a novel approach is presented for removing cyclic dependencies that may result during the plan merging process. We provide theoretical analyses of several algorithms for computing the minimum cost cover of plan fragments, a central component of the methodology. We support the theoretical analysis of these algorithms with experimental data to show that a greedy approach is near-optimal and efficient.

CBPOP: A Domain‐Independent Multi‐Case Reuse Planner

The reuse of multiple cases to solve a single planning problem presents a promise of better utilization of past experience over single‐reuse planning, which can lead to better planning performance. In this paper, we present the theory and implementation of CBPOP, and show how it addresses the multi‐reuse planning problems. In particular, we present novel approaches to retrieval and refitting. We also explore the difficult issue of when to retrieve in multi‐reuse scenarios, and we empirically compare the results of several solutions we propose. Results from our experiments show that the best ranking function for pure generative planning is not necessarily the best ranking function for multi‐reuse planning. The surprising result in the reuse scenarios is that the single‐goal case library performed better than larger case libraries consisting of solutions to multi‐goal problems.

Case-based process planning with hierarchical plan merging

This research focuses on the development of an object-oriented case-based process planner which combines the advantages of the variant and generative approaches to process planning. The case-based process planner operates on general 3D prismatic parts, represented by a collection of features (e.g.: slots, pockets, holes, etc.). Each feature subplan is developed by the case-based planner. Then the feature subplans are combined into the global process plan for the part via a hierarchical plan merging mechanism. Abstracted feature subplans correspond to cases, which are used in subsequent planning operations to solve new problems. The abstracting and storing of feature subplans as cases is the primary mechanism by which the planner learns from its previous experiments to become more effective and efficient.<<ETX>>

An Approach for Merging Plans Hierarchically in Decomposable Domains

Recent works in domain‐independent plan merging have shown that the optimal plan‐merging problem is NP‐hard, and heuristic algorithms have been proposed to generate near‐optimal solutions. These works have shown heuristic algorithms which assume that the mergeability of two actions can be determined by considering only the actions themselves. In this paper, we show that it is often that case that the surrounding actions in the plan must also be considered when determining the mergeability of two or more actions; therefore, the context in which the actions are used affects their mergeability. To address this problem, we have developed a plan‐merging methodology that merges partial‐order plans based on the the notion of plan fragments, which encapsulate actions with the context in which they are being utilized. This methodology applies to a class of planning domains, called decomposable domains, which consist of actions that follow all or some portion of a type sequence. Merging is performed hierarchically by action type. We also investigate the previously unexplored notion of alternative actions in domain‐independent plan merging, which can improve the quality of the resulting merged plan. A novel approach is presented for removing cyclic dependencies that may occur during the plan‐merging process.

Knowledge sharing for planning: the Knowledge Interchange Interface

Cooperation between planners is essential when solving many complicated problems in planning. In this paper, the authors discuss their knowledge sharing methodology for planning which is manifested in a framework, called KII (the Knowledge Interchange Interface). The KII is used to realize cooperation between heterogeneous planning systems. Cooperation takes place in a peer-to-peer manner through a common ontology. The KII consists of five integrated modules which are used to provide knowledge translation into and out of the defined common ontology language from the local knowledge representations and also to provide communication between the planning systems.

Distributed case-based planning: Multi-agent cooperation for high autonomy

A drawback of typical knowledge-based systems, including case-based reasoning systems, is that human intervention is often unavoidable when there is a lack of knowledge necessary to solve a problem. The concept of cooperative case-based reasoning, where a case-based system may interact with another case-based system or other knowledge-based system to acquire the missing knowledge it needs to solve a problem is introduced. The basic mechanism by which this knowledge transfer takes place is the knowledge interface. The knowledge interface translates information in a knowledge-based system and places it into a globally recognized format; likewise, the knowledge interface converts information in the globally recognized format and places it into the context of the local system.<<ETX>>

Computer-Aided Production Planning

The sections in this article are 1 Knowledge Representation 2 Simulation Methodology 3 Automatic Simulation Model Generation 4 Conclusion 5 Appendix 1. Dynamic Models