Kristian J. Hammond. Chef: A model of case-based planning. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pages 261--271. American Association for Artificial Intelligence, 1996. 33  Home/Search   Document Not in Database   Summary   Related Articles   Check

....process of splitting the current execution agent into two separate agents, a query decomposition agent and a task execution agent. The task execution agent will develop execution plans based on user requirements using generarive planning and plan retrieval utilizing case based reasoning techniques [17, 31]. The task execution agent may interleave plauing with information gathering subtasks [2, 34, 23] and repair plans when unexpected situations are encountered [10, 26] Plans will be specified as (transactional) workflows that can be executed by InfoSleuth. It will supervise the execution of the ....

K. J. Hammond, "CHEF: A model of case-based plan- ning " In Proceedings of the Fifth National Conference on Artificial Intelhgence (AAAI-86), Philadelphia, PA, 1986.

....Case based planning is an extension of Sussman s ideas of retrieval and repair and an application of case based reasoning to planning. This approach is also characterized as memory based because the organization of the memory of previous plans is changed during the planning process. CHEF [Ham86a, Ham86b, Ham89] (and WOK [Ham83] is a case based planner that builds new plans out of its memory of old ones. CHEF s domain is Chinese Szechwan cooking and its task is to build new recipes on the basis of users requests. CHEF s input is a set of goals for different tastes, textures, ingredients, and type of ....

....planning loop that allows old plans to be modified in service of new goals. Plan repair, credit assignment are used when constructed plans fail to satisfy the given goals. Plan anticipator uses knowledge collected by credit assignment to predict possible problems during plan construction. Hammond [Ham86a] demonstrated how CHEF works. In planning for the goals to include SNOW PEAS and CHICKEN in a STIR FRY dish, 1. The plan anticipator is reminded of a past failure CHEF encountered in building the BEEF AND BROCCOLI plan. CHEF had tried to stir fry BEEF and BROCCOLI together, which allowing the ....

[Article contains additional citation context not shown here]

Kristian J. Hammond. CHEF : A Model of Case--based Planning. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 267--271, Philadelphia, Pennsylvania, U.S.A., August 11 -- 15 1986. American Association for Artificial Intelligence.

....found during past search. Case based reasoning is well adapted to problems where experience extensively represents domain knowledge and where only a weak domain theory is available [13] Control learning for planning has been investigated in machine learning as well as in case based reasoning [11, 16, 22]. Optimisation differs from planning in nature: First, planning focuses on how to achieve solutions, while in local search optimisation, where solutions are usually constructed easily, the task is to find good ones [10, 23] As consequence, memorising operator chainings, i.e. receipts for ....

Hammond K., CHEF: A model of case-based planning, AAAI, 1986

....3.4 Parsers and other PMs There are many alternatives to the one step at a time process of the SMs described above: any notion of planning and problem decomposition can be turned into a PM. Some of the ones we have been considering are abstraction hierarchies [70] 71] 72] case based planning [28] [29] 37] constraint based planners [80] 81] plan reuse [20] mixedinitiative planning [65] agent planning [2] 59] 67] and others [6] 23] 84] The one we describe here is a distributed planning PM called the Horde Planner (HP) The HP tries to collect together a complete set of ....

Kristian J. Hammond, "CHEF: A Model of Case-Based Planning", pp. 267-271 in Proceedings AAAI'86: The Fifth National Conference on Artificial Intelligence, 11-15 August 1986, Philadelphia, Pennsylvania, AAAI (1986); reprinted in pp. 655659 in [4]

....and test paradigm for error classification is viable. The modified information matrix [8] used to model the relationships between sensors, however, is too complex to extend to domains with larger numbers of heterogeneous sensors. 2. 2 GTD and CHEF Generate Test and Debug (GTD) 1] and CHEF [3] are planning systems which use similar approaches to error recovery in the geological and cooking domains, respectively. GTD uses a generate and test procedure supplemented with a debugger to repair denied hypotheses, creating new ones. One interesting aspect of GTD is that it challenges each of ....

Hammond, K. J., "CHEF: A Model of Case-Based Planning." Proceedings of the National Conference on Artificial Intelligence, 1986, pp. 267-271.

....accurate diagniag Howevermakin a goodkn wledge base is a time cone, A an di#cult process. Therefore the kn wledge acquisition has been recognS A as a bottlen, k ofcon ven tionH expert systems which adopt a production rule base. Model based reasoned [5] ruleine, tion [6] an case basedreasonse [3] have been proposed to overcome this bottlen, k. The CBR has been employed for severaldiagn4: systems [1] 4] 9] because previous troubleshootin experienO isoften available in the field of diagn,S 4 The major advan tages of employin the CBR to the field servicediagne, are: 1. Avoid the kn ....

K.J. Hammond, "CHEF: A model of case-based planning," Proc. AAAI86, pp.267--271, 1986.

.... a used past plan [Hammond 87] or by generating an hypothesis heuristically and then debugging it (GTD generate, test and debug paradigm) Simmons 88] However, a close look at these examples reveals that systems built accordingly have been considered either as early CBR systems (e.g. CHEF [Hammond 86] PERSUADER [Sycara 88] or their precursors (e.g. PLEXUS [Alterman 86] or the techniques developed therein, e.g. plan transformations using indices [Owens 88] and adaptation of explanations [Kass 88, Kass 89] seem to have been taken over by CBR specialists, as historical near ancestors of ....

Hammond K.J.: CHEF : A Model of Case-Based Planning. Proc. AAAI-86, AAAI Press/MIT Press, 1986, pp 267 - 271.

....et al..97] a task goal may require that the final plan contains some specific actions in a specific order (e.g. the user wants to complete the plan: go to Rome, then go to the Opera) a task goal can be seen as a partial plan to complete in conjunction with the global problem goals. Plan reusing [Hammond86] can be seen as a technique for combining and completing partial plans in order to obtain the problem goal. Replanning components are used planning systems which operates in dynamical environments [CurrieTate91] Knoblock95] WangChien97] Replanning system require to be able of completing ....

K.J.Hammond.CHEF: A Model of Case-based Planning. Proc.of AAAI-86, AAAI Press, 1986

....learning are interleaved and the planner stores knowledge that will be useful later, a long term use of space has to be considered. The stored knowledge can take the form of search control rules extracted from problem solving traces, or of cases in a case library [Veloso, 1992, Kambhampati, 1990, Hammond, 1986] Recycling past successful experience reduces the search effort when solving new similar problems. Note that there is usually a trade off among the amount of knowledge stored, the cost of accessing and reusing it, and the savings on search gained from it [Minton, 1988] min search tree) ....

Kristian J. Hammond. CHEF: A model of case-based planning. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 267--271, 1986.

....system requires atomic planning operators. If we had implemented a hierarchical planner, then there would have been a need for abstract planning operators (e.g. 18] Similarly, if we had implemented a script based planner we would have needed to represent scripts or skeletons of plans (e.g. [8]) We note, that although the domain specification would have changed for a hierarchical planner and the script based planner, the ontology would not have to be revised. Both the abstract plans and the scripts can be considered instances of events and processes as defined in the ontology. The ....

Hammond, K.J. (1986). CHEF: A Model of Case-based Planning, Proceedings AAAI-86, pp.267-271, Cambridge, Massachusets, AAAI Press/MIT Press, Cambridge, United States.

....of specific executable actions, perhaps with some conditionality, whose execution will achieve the goals. Then it executes the planned actions. To guard against failures, the agent monitors plan execution and, if necessary, replans. Although there are different methods for constructing plans [2, 16, 38], all operationalize plans as executable programs of action. For present purposes, they are equivalent. The planning model has several strengths. An agent can construct plans that have desirable global properties regarding goals, resources, and side effects, as well as coherence and ....

K. J. Hammond. "Chef: A model of case-based planning." In Proceedings of the National Conference on Artificial Intelligence, San Matao, Ca: Morgan Kaufmann, 1986.

....can be a good idea because it transfers previous solutions rather than searching for it. Since the space of possible plans is typically vast, it is extremely unlikely that a case base contains a plan that can be reused without any modification. First, modification has been addressed in Chef (Hammond 1986), one of the first case based planners. It retrieves cooking recipes and adapts them to the new problem by using domain specific knowledge. As experience has shown, however, this kind of adaptation in realistic domains requires a large amount of very specific domain knowledge and lacks ....

....adaptation methods usually consist of a set of domain dependent knowledge which directly modify the solution contained in the retrieved case, based on the difference between the problem descriptions in the case and of the current problem. While in early case based planning systems (e.g. in Chef, Hammond 1986) only transformational adaptation was used, most recent systems use generative adaptation. For generative adaptation, the integration of a case based problem solving strategy and a generative problem solver is central. The retrieved solution is not modified directly, but is used to guide the ....

Hammond, K. J. (1986). Chef: A Model of Case-Based Planning. pp. 267--271.

....learning community, such as decision tree algorithms [63, 65] and conceptual clustering [26] Explanation based learning [17, 53] is another type of symbolic learning that pushes training examples through a domain theory to create generalized examples for future use. Case based learning techniques [31, 62] and analogical reasoning methods [10] try to map new situations onto previously encountered situations to find the best solution. There are also a wide variety of rule based approaches to concept learning. Information extraction (IE) is a relatively new subfield of natural language processing ....

K. Hammond. CHEF: A model of case-based planning. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 267--271, 1986.

....goal. The only difference in the case of replanning is that the reasoner may also make use of previously computed, and presumably partially correct plans. As researchers in case based reasoning have argued, much of planning itself not just replanning after failure should probably operate thus [3, 27]. The Uses of Plans 5 2.1 The Challenge of Dynamic Environments We so far have a picture in which plans are produced by a reasoning process, a planner, and then are used to guide action. More or less hidden in this picture are some crucial assumptions. One is that an agent s goals are directly ....

K. J. Hammond. CHEF: A model of case-based planning. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 267-- 271, Philadelphia, PA, 1986. The Uses of Plans 40

....(CBP) systems solve this by cacheing solutions to planning problems in a case base, and by providing methods for retrieving solutions whose problems are similar to a new, given problem. Previous CBP research has examined a wide variety of issues including, for example, adaptation strategies (Hammond, 1986), plan structure (Kambhampati Hendler, 1992) similarity metrics for plan reuse (Kambhampati, 1994) indexing structures (Veloso Carbonell, 1993) and techniques for mapping between current situations and retrieved plans (Veloso, 1994) Yet researchers have only recently begun exploring the ....

....a problem, one or more cases are selected which are rated as similar to the problem by somesimilarity function. Adaptation. The retrieved cases are reused to solve the new problem. Three primary adaptation strategies exist. First, heuristic methods use domain knowledge to help guide adaptation (Hammond, 1986). Second, cases in transformational analogy methods (Carbonell, 1983) contain the solution plans; adaptation is performed by refitting these plans relative to the new situation. Finally, cases using derivational analogy approaches (Carbonell, 1986) do not contain the solution plans themselves ....

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Hammond, K. (1986). Chef: a model of case-based planning. Proceedings of Fifth National Conference on Artificial Intelligence (pp. 267--271). Philadelphia, PA: Morgan Kaufmann.

....with previous cases known to the agent, and knowledge is stored as cases rather than as general rules. There are domains, such as the field of AI and law, where this form of reasoning has an obvious attractive; in other domains, such as planning, where case based reasoning showed a lot of promise [ Hammond, 1986 ] it is not competitive with current best approaches (though one should bear in mind that they often tackle different kinds of problems, which makes comparisons harder) Nevertheless, it is an area where research is pursued actively, with such interesting developments as case based decision ....

Kristian Hammond. CHEF: A model of case-based planning. In Tom Kehler and Stan Rosenschein, editors, Proceedings of the 5th National Conference on Artificial Intelligence. Volume 1, pages 267--271, Los Altos, CA, USA, August 1986. Morgan Kaufmann.

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Kristian J. Hammond. Chef: A model of case-based planning. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pages 261--271. American Association for Artificial Intelligence, 1996. 33

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Hammond, K. J. 1996. Chef: A model of case-based planning. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 261--271. American Association for Artificial Intelligence.

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Kristian J. Hammond. CHEF: a model of case-based planning. In Proceedings of the Fourth National Conference on Artificial Intelligence, pages 261--271, 1986.

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Hammond, K.J.: CHEF: A Model of Case-Based Planning. Proc. Fifth National Conference. on Arti#cial Intelligence #AAAI-86#. 1986:267-271.

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Kristian J. Hammond. Chef: A model of case-based planning. In Proceedings of the Thirteenth National Conference on Arti cial Intelligence (AAAI-96), pages 261-271. American Association for Arti cial Intelligence, 1996. 33

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Hammond, K. J.: 1986a, `CHEF: A Model of Case-Based Planning'. In: Proc. Fifth National Conference on Artificial Intelligence. pp. 267--271.

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Kristian J. Hammond. Chef: A model case-based planning. In Proceedings of the Annual Conference of the American Association for Artificial Intelligence (AAAI-86), pages 267--271, 1986.

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K. Hammond. Chef: A model of case-based planning. In Proceedings AAAI86, pages 261--271. AAAI, 1986. Philadelphia, Pennsylvania. 72

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K. Hammond. Chef: A model of case-based planning. In Proceedings AAAI-86, pages 261--271. AAAI, 1986. Philadelphia, Pennsylvania.

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