{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:36:33Z","timestamp":1767638193364,"version":"3.48.0"},"reference-count":54,"publisher":"Maximum Academic Press","issue":"2","license":[{"start":{"date-parts":[[2007,6,1]],"date-time":"2007-06-01T00:00:00Z","timestamp":1180656000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2007,6]]},"abstract":"<jats:title>Absract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the \u2018syntactic\u2019 structure of the problem space and of the search tree. Therefore, they do not need any \u2018semantic\u2019 information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain \u2018good\u2019 solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.<\/jats:p>","DOI":"10.1017\/s0269888907001075","type":"journal-article","created":{"date-parts":[[2007,7,5]],"date-time":"2007-07-05T07:25:24Z","timestamp":1183620324000},"page":"153-184","source":"Crossref","is-referenced-by-count":3,"title":["<scp>PLTOOL<\/scp>\n                    : A knowledge engineering tool for planning and learning"],"prefix":"10.48130","volume":"22","author":[{"given":"Susana","family":"Fern\u00e1ndez","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Borrajo","sequence":"additional","affiliation":[]},{"given":"Raquel","family":"Fuentetaja","sequence":"additional","affiliation":[]},{"given":"Juan D.","family":"Arias","sequence":"additional","affiliation":[]},{"given":"Manuela","family":"Veloso","sequence":"additional","affiliation":[]}],"member":"27968","published-online":{"date-parts":[[2007,6,1]]},"reference":[{"volume-title":"Working notes of the ICAPS\u20190 5Workshop on Role of Ontologies in Planning and Scheduling","year":"2005","author":"Arias","key":"S0269888907001075_ref005"},{"volume-title":"Proceedings of the Seventeen International Florida Artificial Intelligence (FLAIRS04)","year":"2004","author":"Fern\u00e1ndez","key":"S0269888907001075_ref022"},{"key":"S0269888907001075_ref027","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1613\/jair.855","article-title":"The FF planning system: fast plan generation through heuristic search","volume":"14","author":"Hoffmann","year":"2001","journal-title":"Journal of Artificial Intelligence Research"},{"volume-title":"Working notes of the IJCAI\u201901 Workshop on Planning with Resources","year":"2001","author":"Borrajo","key":"S0269888907001075_ref011"},{"volume-title":"Automated task planning. theory and practice","year":"2004","author":"Ghallab","key":"S0269888907001075_ref025"},{"key":"S0269888907001075_ref024","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(72)90051-3"},{"volume-title":"Proceedings of the Sixth International Conference on Artificial Intelligence Planning Systems (AIPS-02)","year":"2002","author":"Aler","key":"S0269888907001075_ref001"},{"volume-title":"Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI\u201987)","year":"1987","author":"McCluskey","key":"S0269888907001075_ref036"},{"volume-title":"Proceedings of the Ninth National Conference on Artificial Intelligence","year":"1991","author":"Knoblock","key":"S0269888907001075_ref033"},{"volume-title":"Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling","year":"2000","author":"Ambite","key":"S0269888907001075_ref004"},{"volume-title":"Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97)","year":"1997","author":"Estlin","key":"S0269888907001075_ref020"},{"key":"S0269888907001075_ref009","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(01)00108-4"},{"volume-title":"Proceedings of the Ninth International Conference on Machine Learning","year":"1992","author":"Etzioni","key":"S0269888907001075_ref021"},{"key":"S0269888907001075_ref053","unstructured":"Yang Q. , 2005 Learning action models from plan examples with incomplete knowledge. 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