Earl D. Sacerdoti. The nonlinear nature of plans. Proc. International Joint Conference on Artificial Intelligence (IJCAI), page 206--214, 1975.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....operators in N, each of which has been proven to be free from interference in S. The case WM is assumed to be globally available throughout the executor, and describes the state of the agent s environment during execution. 5 Related Work The current paradigm for plan generation begins with NOAH (Sacerdoti, 1975) and NonLin (Tate, 1977) this approach has been extended by others (Vere, 1981; Wilkins, 1984, Currie Tate, 1985) but the core idea remains the same. A planner searches through a space of incomplete plans, each of which is partially ordered. Each plan is incomplete in the sense that it must be ....

Earl Sacerdoti. The Non-Linear Nature of Plans. In Proceedings of the Fourth Joint Conference on Artificial Intelligence, pages 206--214, Tbilisi, Georgia, USSR, 1975. International Joint Committee on Artificial Intelligence.

....The following list summarizes some of these mech anisms: 1. Plan synthesis using domain knowledge and such principles as means ends analysis and precondition achievement have been used since the early days of AI. These principles have been widely used by planning systems such as STRIPS [3] NOAH [11] and their descendents. Plan synthesis reduces future deliberation and helps the agent achieve its goals . 2. Projection of possible future world states using domain knowledge. Such projections can improve performance by taking actions that correspond to anticipated events and by taking actions ....

E. D. Sacerdoti. The Non-Linear Nature of Plans, Pro- ceedings of the International Joint Conference on Artificial Intelligence, Tibilisi, USSR, 1975.

....was supported in part by NSF Grants IRI 9306580 and EEC 9402384, and ONR grant N0001491 J 1451. Any opinions, ndings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily re ect the views of the National Science Foundation or ONR. noah(Sacerdoti, 1975), nonlin (Tate, 1977) deviser (Vere, 1983) and sipe (Wilkins, 1988) Until now, there has been very little analytical work on the properties of HTN planners. One of the primary obstacles impeding such work has been the lack of a clear theoretical framework explaining what a HTN planning system ....

....has a driver s licence. 2.1.1 Alternative Views of Non Primitive Tasks There appears to be some general confusion about the nature and role of tasks in htn planning. This appears largely due to the fact that htn planning emerged, without a formal description, in implemented planning systems (Sacerdoti, 1975; Tate, 1977) Many ideas introduced in htn planning (such as nonlinearity, partial order planning, etc. were formalized only as they were adapted to strips style planning, and only within that context. Those ideas not adapted to strips style planning (such as compound tasks and task ....

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Sacerdoti, E. D. The nonlinear Nature of Plans In Proceedings of IJCAI, 1975. pp 206|214.

....of the PRODIGY architecture [Carbonell et al. 1990, Veloso, 1989] develops a method to solve problems nonlinearly that explores different alternatives at the operator and at the goal ordering levels. Commitments are made during the search process, in contrast to a least commitment strategy [Sacerdoti, 1975, Tate, 1977, Wilkins, 1989] where decisions are deferred until all possible interactions are recognized. With the casualcommitment approach [Minton et al. 1989] background knowledge, whether hand coded expertise, learned control rules, or heuristic evaluation functions, guides the efficient ....

Earl D. Sacerdoti. The nonlinear nature of plans. In Proceedings of IJCAI-75, pages 206--213, 1975.

....(a total order plan) Problems occur when partial plans interact with one another. For example, plan segments satisfying subgoals A and B might work fine when taken in isolation, but they may conflict when taken together, preventing both goals from being simultaneously satisfied (see, e.g. 22] [27], 40] 41] Hierarchical planners are one solution to the difficulties encountered in these early systems (see, e.g. 26] 28] 37] Very briefly, planning is carried out at several levels of abstraction. High level or abstract plans are used to generate skeletal or outline solutions. As ....

E.D. Sacerdoti, "The Non-Linear Nature of Plans," Proc. Fourth Int'l Joint Conf. Artificial Intelligence, 1975.

.... an extensive related literature, treating in turn the areas of temporal projection (e.g. Gelfond, 1988, Georgeff, 1987, Haugh, 1987, Kautz, 1986, Lifschitz, 1987a, Lifschitz, 1987b, Morris, 1988, Pearl, 1988, Shoham, 1988b, Baker, 1989 ] plan interaction (e.g. Sussman, 1973, Tate, 1975, Sacerdoti, 1975, Vere, 1983 ] and meta planning (e.g. Russell and Wefald, 1989, Brooks, 1991, Brooks, 1986, Doyle, 1988, Pollack and Ringuette, 1990, Ingrand and Georgeff, 1990 ] We have not sought to build an optimal planner, not even a state of the art planner; there are manyways to make the ....

Earl Sacerdoti. The non-linear nature of plans. In Advance papers for IJCAI -- 75, 1975.

.... after an interruption because a complete plan is available at the end of each iteration (although the plan may not fully satisfy the agent s goals) Another approach to planning that can provide a complete plan at the end of each cycle of its operation is hierarchical task network (HTN) planning [14]. An HTN planner works by reducing an abstract representation of an action into less abstract parts, and then those parts into parts that are less abstract again. This continues until the plan is totally composed of primitive tasks. Primitive tasks are the atomic tasks that the agent can execute ....

E. D. Sacerdoti. The nonlinear nature of plans. In J. Allen, J. Hendler, and A. Tate, editors, Readings in Planning, pages 162-170. Morgan Kaufmann, 1990.

....the planning problem for the planner to use in its search. Other systems operate by doing a forward chained search and using user supplied information to prune aggressively. 3.2. 1 Hierarchical Task Networks The earliest hierarchical task network (HTN) planning system was Sacerdoti s NOAH planner [55]. NOAH introduced the concept of partially ordered planning; that is, orderings would only be added between operators when a conflict of some kind was detected between them. It also added the concept of a critic that could make constructive improvements to a plan being developed. Task specific ....

Earl D. Sacerdoti. The nonlinear nature of plans. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence, pages 206--214, 1975.

....nature from the lifting lemma of resolution [Fikes and Nilsson, 1971] Robinson, 1965] In addition to being lifted, most modern planners are nonlinear they maintain a partial order on plan steps rather than a total order. This partial order is gradually refined during the planning process [Sacerdoti, 1975], Sacerdoti, 1977] Tare, 1977] Chapman, 1987] Finally, some planners use abstraction spaces in which planning is first done at a high level of abstraction and then low level details are filled in once a high level plan has been found [Sacerdoti, 1974] Korf, 1987] Yang and Tenenberg, ....

Earl D. Sacerdoti. The nonlinear nature of plans. In IJCAI75, pages 206-214, 1975.

....nature from the lifting lemma of resolution [Fikes and Nilsson, 1971] Robinson, 1965] In addition to being lifted, most modern planners are nonlinear they maintain a partial order on plan steps rather than a total order. This partial order is gradually refined during the planning process [Sacerdoti, 1975], Sacerdoti, 1977] Tate, 1977] Chapman, 1987] Finally, some planners use abstraction spaces in which planning is first done at a high level of abstraction and then low level details are filled in once a high level plan has been found [Sacerdoti, 1974] Korf, 1987] Yang and Tenenberg, ....

Earl D. Sacerdoti. The nonlinear nature of plans. In IJCAI75, pages 206-214, 1975.

....approach of a human expert in a certain field, by breaking the system into smaller subsystems. These ideas have been implemented for many problems with varying degrees of success [1, 2, 15] Implementations based on the formal theories of linear and nonlinear planning meet hard efficiency problems [4, 12, 17, 22, 25]. An efficient planner requires an intensive use of heuristic knowledge. On the other hand, a pure heuristic implementation is unique. There is no general constructive approach to such implementations. Each new problem must be carefully studied and previous experience usually can not be applied. ....

.... to the research on programmed attribute grammars by Knuth [11] Rozenkrantz [21] Volchenkov [35] A mathematical environment (a glue ) for the formal implementation of this approach was developed following the theories of formal problem solving and planning by Nilsson [17] Fikes [7] Sacerdoti [22], McCarthy, Hayes [13, 14] and others based on first order predicate calculus. To show the power of the linguistic approach it is important that the chosen model of the heuristic hierarchical system be sufficiently complex, poorly formalized, and have successful applications in different areas. ....

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Sacerdoti, E.D. (1975) "The Nonlinear Nature of Plans," Proc. Int. Joint Conference on Artificial Intelligence.

....reality world, there is less of a naming problem and they focus on stronger NL parsing approaches, using knowledge about 163 the current situation to filter nonsensical parses. 6.4. 3 Execution and Monitoring IPEM (Integrated Planning, Execution and Monitoring) is a nonlinear planner (in the NOAH [71] and TWEAK [17] tradition) with execution monitoring and replanning capabilities [4] The execution module has access to the complete plan structure (with all the protected links, etc. and notices when unexpected events either remove a plan step s preconditions or make it redundant (by achieving ....

....the unique identity of the wrench. Instead, the plan will include a step to locate the wrench, once it has entered the room, and then have a step to pick up the wrench that it found there. EXCALIBUR is a system in which qualitative process theory is used with a typical state based planner (Nonlin [71]) to better reason about domains where there are continuous processes [24] Qualitative process theory allows a reasoner to deal correctly with the indirect e#ects of actions the action of opening a faucet leads to the condition of the faucet being open, but a state based planner will have ....

Earl D. Sacerdoti, The nonlinear nature of plans, Fourth International Joint Conference on Artificial Intelligence, 1975.

....agent wants to do them. The second set consists of representations of such properties of the world as the tendency of unsupported objects to fall to the ground. They are known here as shifts, and require no motivation on any agent s part. The case addressed by classical AI planners (for example, [95, 48, 113, 20]) is that of planning in a domain in which there is only a single agent, and in which the STRIPS assumption holds [134] that is, in which nothing can change except as the result of the 2 The conditions of the action rule may include an agent s desire to act, but for the purpose of naive ....

....classical planning (including the Sussman anomaly) which had solutions, but for which no hierarchy of operations would guarantee that a solution could be found. These were solved by the more general approach of nonlinear planning. The first system to make use of this technique was Sacerdoti s NOAH [113], and its full potential was used in Tate s NONLIN [129] In a nonlinear planner, the order of operations is only partially defined as long as the plan is incomplete. The planning process involves putting constraints on the order of the operations as well as adding operations to the plan. ....

Earl D. Sacerdoti. The nonlinear nature of plans. In Advance Papers of the Fourth International Joint Conference on Artificial Intelligence, pages 206--214, 1975.

No context found.

Earl D. Sacerdoti. The nonlinear nature of plans. Proc. International Joint Conference on Artificial Intelligence (IJCAI), page 206--214, 1975.

No context found.

Earl D. Sacerdoti. The nonlinear nature of plans. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence, pages 206--214. Morgan Kaufmann, 1975. Reprinted in [3].

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E. Sacerdoti. The nonlinear nature of plans. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 206--214, 1975.

No context found.

E.D. Sacerdoti. The nonlinear nature of plans. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence, 1975.

No context found.

E. D. Sacerdoti. The nonlinear nature of plans. In Proceedings of the 4th International Joint Conference on Artificial Intelligence (IJCAI-75), pages 206--214, 1975.

No context found.

E. Sacerdoti. The nonlinear nature of plans. In Proc. IJCAI-75, 1975.

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Sacerdoti, E. D.: 1975, `The Nonlinear Nature of Plans'. In: Proc. Fourth Intl. Joint Conference on Artificial Intelligence. pp. 206--214.

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E. Sacerdoti. The nonlinear nature of plans. In Proc. IJCAI-75, 1975.

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E. Sacerdoti. The nonlinear nature of plans. Proceedings of the fourth International Joint Conference on Arti cial Intelligence, pages 206{ 214, 1975.

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E.D.Sacerdoti, The non-linear nature of plans, Proc. 4th IJCAI, 1975.

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Sacerdoti, Earl D., "The Nonlinear Nature of Plans", 1975, Advance Papers of the Fourth IJCAI.

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E.D. Sacerdoti, The Nonlinear Nature of Plans. In Proceedings of International Joint Conference of Artificial Intelligence, 1975.

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