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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i6odselg2vhytkiryfsdh6fpjy" style="color: black;">IEEE Intelligent Systems and their Applications</a>
This article illustrates the complexities of real-world planning and how w e can create AI planning systems to address them. We describe the IMACS Project (Interactive M a n ufacturability Analysis and Critiquing System) from the University of Maryland, College Park. IMACS is an automated designer's aid to evaluate the manufacturability of machined parts and suggest design modications to improve manufacturability. Over the course of our eorts on IMACS the manufacturing domain continually<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/5254.683210">doi:10.1109/5254.683210</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fwnyj72alzc3pgilswg7mxmp2u">fatcat:fwnyj72alzc3pgilswg7mxmp2u</a> </span>
more »... ged us to come up with working solutions that would scale to realistic problems. This paper compares and contrasts IMACS's planning techniques with those used in classical AI planning systems and describes (1) how some of IMACS's planning techniques may be useful for AI planning in general, and (2) what challenges need to be overcome by AI planners so that they can be successfully used in manufacturing process planning. Similarities between AI planning techniques and IMACS planning techniques indicate the large unrealized potential of AI planning techniques in solving real-world manufacturing problems. On the other hand, dierences seem to indicate the need for domain-specic planning techniques. In particular, our experience suggests that process planning for complex machined parts would not be accomplished very well by populating a general purpose planner with domain-specic knowledge. Instead, we needed to integrate the domain-specic knowledge into the planning algorithms themselves.
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