Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974. Home/Search Document Not in Database Summary Related Articles Check |
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Domain-Dependent Knowledge in Answer Set Planning - Tran Cao Son (2002) (Correct)
....the use of the concurrent construct k from [12] where pkq represents the set consisting of two programs p; q and q; p, is not very helpful either. This deficiency of pure procedural constructs of the type discussed in the previous section prompted us to look at the constructs in HTN planning [39]. The partial ordering information allowed in HTN descriptions serves the purpose. Thus all we need is to have the constraint that says p 1 must occur before p 2 . The constructs in HTN by themselves are not expressive enough either as they do not have procedural constructs such as procedures, ....
E. D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
Planning with Complex Actions - McIlraith, Fadel (2002) (3 citations) (Correct)
....with complex actions can dramatically improve the efficiency of plan generation by reducing the search space size and the length of a plan. The idea of planning with some form of abstraction or aggregation is not new, and there has been a variety of work in this area including ABStrips (e.g. [17]) planning with macro operators (e.g. 11] and [6] and most notably HTN planning (e.g. 5] Our work is fundamentally different from these approaches, and in particular from HTN planning, both in terms of i) the representation of complex actions (aka HTN non primitive tasks) and ii) the ....
E. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
A Hybrid Task Planner Architecture For Pick And Place - Xoh (2000) (Correct)
....hierarchical, skeletal and opportunistic [3] Non hierarchical planners use goals directly to find operators as in NOAH [4] and STRIPS [5] planners. In hierarchical planners, planning begins at an abstract level, but later abstract goals are expanded into more detailed subgoals as in ABSTRIPS [6], NONLIN [7] and DEVISER [8] Skeletal planners store successful earlier plans in a plan database. Before planning begins, goals are compared against the skeletal plans. If one or more plans in the database satisfy the current goals and the current world model, the best plan will be chosen as in ....
E. Sacerdoti, "Planning in a Hierarchy of Abstraction Spaces", Artificial Intelligence, V. 5, pp. 115-135, 1974.
Decision-Theoretic Control of Planetary Rovers - Zilberstein, Washington.. (2002) (Correct)
....criterion is that it is a quantity to be maximized, not a discrete goal to be achieved. This differs markedly from traditional applications of planning technology to mobile robotics. From the early days of planning, applications to robotics have typically concentrated on achieving discrete goals [18, 15, 28]. More recently, decision theoretic planning has extended beyond all or none goals to handle overall reward [21, 29] offering a more suitable framework for planetary rover control. Autonomous control of rovers on distant planets is necessary because the roundtrip time for communication makes ....
Sacerdoti, E.D.: Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence 5(2) (1974) 115--135
Domain-Dependent Knowledge in Answer Set Planning - Son, Baral, Nam, McIlraith (2002) (Correct)
....the use of the concurrent construct k from [12] where pkq represents the set consisting of two programs p; q and q; p, is not very helpful either. This de ciency of pure procedural constructs of the type discussed in the previous section prompted us to look at the constructs in HTN planning [39]. The partial ordering information allowed in HTN descriptions serves the purpose. Thus all we need is to have the constraint that says p 1 must occur before p 2 . The constructs in HTN by themselves are not expressive enough either as they do not have procedural constructs such as procedures, ....
E. D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Arti cial Intelligence, 5:115-135, 1974.
Experimenting The Performance Of Abstraction - Mechanisms Through Parametric (2003) (Correct)
....The former combines a group of actions to form macro operators [1] whereas the latter exploits representations of the world given at a lower level of detail. The most significant forms of state based abstraction rely on (i) relaxed models, obtained by dropping operators applicability conditions [2], and on (ii) reduced models, obtained by completely removing certain conditions from the problem space [3] Both models, while preserving the provability of plans that hold at the ground level, perform a weakening of the original problem space, thus suffering from the drawback of introducing ....
E.D. Sacerdoti, Planning in a hierarchy of abstraction spaces, Artificial Intelligence, 5, 1974, 115--135.
Hierarchical Case-Based Reasoning Integrating Case-Based .. - Smyth, Keane, Cunningham (2001) (1 citation) (Correct)
.... 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 planning proceeds, these abstract plans are refined by replacing abstract operators with collections of more detailed ....
E.D. Sacerdoti, "Planning in a Hierarchy of Abstraction Spaces," Artificial Intelligence, vol. 5, no. 2, pp. 115-135, 1974.
Abstracting Visual Percepts to Learn Concepts - Zucker, Bredeche, Saitta (2002) (1 citation) (Correct)
....of the image. The higher the resolution, the finer the detail that can be discerned. The definition corresponds to the clarity of detail in an image and is dependent upon resolution and contrast [11] We will depart from the traditional use of the term ground for the initial representation [14] as for a robot the notion of grounding corresponds to another notion 8 Fig. 3. The space of image representation obtained by applying the associate operator (changing the resolution) and the aggregate operator (changing the structure) Three examples of representations obtained after having ....
Sacerdoti, E.: Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5 (1974) 115--135
A Conversational Agent To Help Navigation And Collaboration - Bersot, Guedj, Godéreaux, .. (1998) (Correct)
....instance, marcher (walk) consists in extending one leg, then the other one and doing it again. Figure 3. Agent hierarchy. 6.1. Generating a plan. We have implemented a linear and hierarchical motion planner that decomposes a problem into a sequence of independent more basic sub problems [23] [24]. From the set of problems, the planner builds a chain of operators describing the intermediary positions of the virtual agent. The chain is finally submitted to a cinematic simulator that fractionates it into 3D geometric actions to yield a relatively fluid motion. The plan generator receives a ....
Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
Experimenting Abstraction Mechanisms through an.. - Armano, Cherchi, Vargiu (2002) (Correct)
....The former combines a group of actions to form macro operators [7] whereas the latter exploits representations of the world given at a lower level of detail. The most significant forms of state based abstraction rely on (i) relaxed models, obtained by dropping operators applicability conditions [10], and on (ii) reduced models, obtained by completely removing certain conditions from the problem space [6] Both models, while preserving the provability of plans that hold at the ground level, perform a weakening of the original problem space, thus suffering from the drawback of introducing ....
Sacerdoti, E.D., Planning in a hierarchy of abstraction spaces, Artificial Intelligence, 5 (1974) 115 --135.
Linear Logic Programming for AI Planning - Küngas (2002) (Correct)
....than the whole search space. The rst explicit use of abstraction in automated deduction was in the planning version of GPS [80] GPS did not create abstractions, but automatically generated di erence orderings, which specify the order in which to work on the various goal conditions. ABSTRIPS [88] was the rst system that attempted to automate the formation of abstraction spaces, but only partially automated the process. Besides, ABSTRIPS produced only relaxed models [56] by abstracting away literals in preconditions (antecedents of sequents in LL sequent calculus) thus the problem space ....
E. D. Sacerdoti. Planning in a Hierarchy of Abstraction Spaces. Arti cial Intelligence, Vol. 5, pp. 115-135, 1974.
Anytime Replanning Using Local Subplan Replacement - Ferrer (2002) (Correct)
....are NP or PSPACE Complete [33] 17] or undecidable [18] A large number of different approaches and heuristics that have appeared in the planning literature attempt to make planning practical. All these approaches attempt to find a way to limit the space that is searched for a plan. Sacerdoti [54] devised a system that began by creating abstract plans. Detailed plans were created by his system once the abstract plans were complete. Plans with unsuitable abstractions were ignored by the search process, a considerable computational savings. Many planning systems (e.g. 9] 42] 40] use ....
Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
Systematic Nonlinear Planning - McAllester, Rosenblitt (1991) (224 citations) (Correct)
.... 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, 1990] Nonlinear planners are sometimes called least commitment planners . In gen eral, the informal principle of least commitment states that one should should make low commitment choices before making high commitment choices. Lifting is a good example of ....
Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelli#ence, 5:115-135, 1974. 11
On Extracting Goal Structure from Planning Domain.. - McCluskey, Porteous (1995) (Correct)
....generator called PROPOSE and a method for generating possible orders between goals based on [8] In this section we will present the results of a selection of these tests to give a feel for the potential of our approach. STRIPS world Results We used two variants of Sacerdoti s STRIPS world [16]. Firstly, a STRIPS world consisting of 7 rooms connected by 10 doors in which a robot is capable of opening doors and moving any of 3 different boxes. The domain model consists of 8 schema representing actions, 20 inconsistency expressions, 70 immutable facts, 6 inference rules and a set of 20 ....
E. Sacerdoti. Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence, 5, 1974. 12
Linear Logic Theorem Proving With Abstraction - Küngas (2002) (Correct)
....Then one can see that if at every abstraction level exactly one new axiom is presented (l a = l h ) the exponential complexity is reduced to linear. 6 Related work and conclusions The rst explicit use of abstraction in automated deduction was in the planning version of GPS [12] ABSTRIPS [15] was the rst system that attempted to automate the formation of abstraction spaces, but only partially automated the process. Besides, ABSTRIPS produced only relaxed models [7] by abstracting away literals in preconditions, thus the problem space was not abstracted at all. In contrary Knoblock s ....
E. D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Arti cial Intelligence, 5:115-135, 1974. 11
Systematic Nonlinear Planning - McAllester, Rosenblitt (1991) (224 citations) (Correct)
.... 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, 1990] Nonlinear planners are sometimes called least commitment planners . In gen eral, the informal principle of least commitment states that one should should make low commitment choices before making high commitment choices. Lifting is a good example of ....
Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115-135, 1974. 11
Automatically Generating Abstractions for Problem Solving - Knoblock (1991) (56 citations) (Correct)
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
Learning Abstraction Hierarchies for Problem Solving - Knoblock (1990) (46 citations) (Correct)
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
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Sacerdoti, E. D. (1974). Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5, 115--135.
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Sacerdoti, E. D. 1974. Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5:115--135.
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Sacerdoti, E. D., 1974. Planning in a hierarchy of abstraction spaces, Artificial Intelligence 5, 115-135.
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Sacerdoti, E. D., 1974. Planning in a hierarchy of abstraction spaces, Artificial Intelligence 5, 115-135.
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
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Sacerdoti, E. D. 1974. Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5(2):115--135.
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
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Earl Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2), pages 115--135, 1974.
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E.D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
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E. D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
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E. Sacerdoti, "Planning in a hierarchy of abstraction spaces," Artificial Intelligence, Vol. 5, No. 2, 115 - 135, 1974.
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E.D. Sacerdoti. Planning in a Hierarchy of Abstraction Spaces. In Proceedings of the 3rd International Joint conference on Artificial Intelligence, 1973. 15
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Arti cial Intelligence, 5(2):115-135, 1974.
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E. D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
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Earl D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115--135, 1974.
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Earl Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115-- 135, 1974.
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Sacerdoti, E. D. (1974). Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5:115--135.
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E.D. Sacerdoti, Planning in a hierarchy of abstraction spaces, Artificial Intelligence, 5, 1974, 115--135.
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E. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
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E. Sacerdoti. Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence, 5(2), 1975.
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Sacerdoti, E. (1974). Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5 (pp. 115-135). 2.
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E.D. Sacerdoti, "Planning in Hierarchy of Abstraction Spaces", Artificial Intelligence, 5, 115-- 135 (1974)
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E.D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115--135, 1974.
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Sacerdoti, E. (1974). Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence, 5(?), 115-135.
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E. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5, 1974.
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Sacerdoti, E. (1974) Planning in a hierarchy of abstraction spaces, Artificial Intelligence, 5, pp. 115-135, Amsterdam, Netherlands: North Holland.
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