Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National ConferenceonArtificial Intelligence, pages 923--928, Boston, MA, 1990.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....problem to be solved (a theorem to be proved) this algorithm reformulates the original problem into more abstract ones. The original problem may be mentioned in the further text also as the base problem, because it is determined at the lowest abstraction level. The ordered monotonicity property [55] is used as the basis for generating abstraction hierarchies. This property captures the idea that as an abstract solution is re ned, the structure of the abstract solution should be maintained. The process of re ning an abstract solution requires inserting additional steps to achieve the ....

....solution is re ned, the structure of the abstract solution should be maintained. The process of re ning an abstract solution requires inserting additional steps to achieve the literals ignored at more abstract level. The ordered monotonicity property of an abstraction hierarchy is de ned in [55] as follows. De nition 2.4.1 Ordered monotonic re nement is a re nement of an abstract solution that leaves the truth value of every literal in an abstract space unchanged. De nition 2.4.2 Ordered monotonic hierarchy is an abstraction hierarchy with the property that for every solvable ....

C. A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Arti cial Intelligence, Boston, MA, AAAI Press, pp. 923-928, 1990.

....as well as helping to explore and refine the engineering of the domain. One particular area that seems to be neglected is of pre compiling domain specifications to extract the goal structure within them (notable exceptions include analysis of goal interactions in REFLECT [4] STATIC [6] and ALPINE [8], as well as our own earlier work [11] Indeed, Barrett and Weld conclude that a domain compiler which identified good and bad orderings would have considerable benefits [1, pp 103] In this paper we extend our work reported in [11] and present the framework for an analysis of goal establishment ....

.... planning ffl use of type (iii) interactions to debug the domain model We have extensively tested the impact of using type (i) goal orders in plan generation in conjunction with a macro operator 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 ....

C. A. Knoblock. Learning Abstraction Hierarchies for Problem Solving. In Proc. AAAI, 1990.

....axioms and a sequent to be proved this algorithm reformulates the original problem into more abstract ones. The original problem is referred to in the subsequent text also as the base problem, because it is determined at the lowest abstraction level. The ordered monotonicity property [6] is used as the basis for generating abstraction hierarchies. This property captures the idea that as an abstract solution is re ned, the structure of the abstract solution should be maintained. The ordered monotonicity property of an abstraction hierarchy is de ned in [6] as follows. The ....

....monotonicity property [6] is used as the basis for generating abstraction hierarchies. This property captures the idea that as an abstract solution is re ned, the structure of the abstract solution should be maintained. The ordered monotonicity property of an abstraction hierarchy is de ned in [6] as follows. The ordered monotonic re nement is de ned as a re nement of an abstract solution that leaves the truth value of every literal in an abstract space unchanged. Algorithm DetermineConstraints(graph; axioms; goal) output: constraints, which guarantee ordered monotonicity for a given ....

C. A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Arti cial Intelligence, Boston, MA, 1990.

....succeeding steps (in a state space encoding) or apply a restriction on the number of steps before an action can assert a new value for the variable (in a causal encoding) 4. 1 Related Work There are a variety of other systems for creating and exploiting abstraction hierarchies [ Sacerdoti, 1974; Knoblock, 1990; Allen et al. 1991 ] ALPINE in particular has the ordered monotonicity property [ Knoblock, 1990 ] So, ALPINE s lower level plans will not disturb any predicates at steps committed at higher abstraction levels. Although this property restricts the abstraction steps ALPINE can take, using a ....

....action can assert a new value for the variable (in a causal encoding) 4. 1 Related Work There are a variety of other systems for creating and exploiting abstraction hierarchies [ Sacerdoti, 1974; Knoblock, 1990; Allen et al. 1991 ] ALPINE in particular has the ordered monotonicity property [ Knoblock, 1990 ] So, ALPINE s lower level plans will not disturb any predicates at steps committed at higher abstraction levels. Although this property restricts the abstraction steps ALPINE can take, using a system with this property in CPlan would have a substantial benefit. With the ordered monotonicity ....

C. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, pages 923--928. Menlo Park, Calif.: AAAI Press, August 1990.

....on different layers, each one equipped with a suitable planner. Each planner is allowed to use only the operators available at the corresponding level. It is worth pointing out that, whereas typical hierarchical planners use a fixed (a priori) decomposition strategy (e.g. HTN [8] ABSTRIPS [9]) in the proposed architecture, the decomposition is performed at runtime and distributed on several layers. Each layer is devoted to perform a (local) planning on any goal imposed by its overlying planner (if any) or to perform a re planning activity, if needed. Thus, a goal to be attained at ....

Knoblock, C.A., "Learning Abstraction Hierarchies for Problem Solving", in Proceedings of the Height National Conference on AI, pp. 993928, AAAI Press, Menlo Park, CA, 1991.

....1993) Others have studied re using plans under other conditions (Hammond, 1990) including multiple actors (Suguwara, 1995) An actor can also create a plan from scratch using a given set of STRIPS like operators (Fikes Nilsson, 1971) in a hierarchical planner (c.f. Sacerdoti, 1974; Knoblock, 1990) The situation calculus (McCarthy, 1958: 1968) is used to represent the planner s expectations about changes in the situation that result from a given kind of action. Each actor maintains a representation of the probability of success for di#erent actions in di#erent contexts. When planning ....

Knoblock, C. A. (1990). Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, pages 923--928.

....lower level tools. Actually, we have to learn how we can formally represent, generate, and investigate a mathematical model based on the abstract images extracted from the expert vision of the problem. implemented for many problems with varying degrees of success (see, e.g. Albus, 1991; Knoblock, 1990; Mesarovich et al., 1989; Botvinnik, 1984) Implementations based on the formal theories of linear and nonlinear planning meet hard efficiency problems (McAllester and Rosenblitt, 1991; Chapman, 1987; Nilsson, 1980; Stefik, 1981; Sacerdoti, 1975) An efficient planner requires an intensive use of ....

Knoblock, C.A. (1990). Learning Abstraction Hierarchies for Problem Solving, Proc. of the 8th AAAI Conf., (pp.923-928), Menlo Park, CA.

....2. Tools used primarily for compiling a domain model into a more efficient or operational form. These include: a) macro generation (see section 4.1 below) b) generation of various types of goal orderings (see section 4. 2 below) c) abstraction hierarchy generation (for example ALPINE in [35, 36]) At present our environment contains a number of tools of category 1 (a, b, e and f) as follows: ffl a tool that uses the substate class definitions to: check the syntax of the domain operator set; check that all substates that should be achievable are indeed achievable by operator action; help ....

....the orders were generated. For example, there is no precondition to do 42 with inflating a tyre before putting it on a hub in the operators, yet this might be a valid constraint. 5.3. 3 Generation of Possible Goal Orderings ABGEN is an implementation based on the ALPINE algorithm presented in [35, 36], which generates goal orders on the basis of possible interactions between goals (ABGEN was described in section 4.2) We included it in the tests as it complements necessary orders when choosing which goal to establish next during planning: given a set of goal predicates, necessary orders reduce ....

C. A. Knoblock. Learning Abstraction Hierarchies for Problem Solving. In Proc. AAAI, 1990.

....incorporating hierarchies into reinforcement learning algorithms. Research in classical planning has shown that hierarchical methods such as hierarchical task networks (Currie Tate, 1991) macro actions (Fikes, Hart, Nilsson, 1972; Korf, 1985) and state abstraction methods (Sacerdoti, 1974; Knoblock, 1990) can provide exponential reductions in the computational cost of finding good plans. However, all of the basic algorithms for probabilistic planning and reinforcement learning are flat methods they treat the state space as one huge flat search space. This means that the paths from the start ....

Knoblock, C. A. (1990). Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 923--928 Boston, MA. AAAI Press.

.... one with conditional effects and universal quantification Inadequate emphasis on task decomposition has also had an unfortunate effect on machine learning research for planning, e.g. explanation based learning (Minton 1988) static domain analysis (Etzioni 1993, Smith Peot 1993) abstraction (Knoblock 1990, Yang Tenenberg 1990) case based planning (Hammond 1990) and derivational analogy (Veloso Carbonell 1993) It is unfortunate that the vast majority 1 of research on speedup learning has ignored taskdecomposition planners, since defining and using tasks provides a very successful form of ....

....steps respectively. The purpose of our experiment was to explore interactions between plan parsing and three different search strategies: vanilla best first; best first search with a search space structured according to an abstraction hierarchy generated by the alpine machine learning algorithm (Knoblock 1990); and domain dependent, hand coded rules. To create our Tyreworld parser we defined 5 tasks for getting tools, inflating tires, removing tires, installing tires, and cleaning up. The Process Planning parser was created out of a set of 12 schemata defining tasks like setting up drill presses and ....

Knoblock, C. 1990. Learning abstraction hierarchies for problem solving. In Proc. 8th Nat. Conf. on A.I., 923--928.

....Carbonell, 1993) Others have studied re using past plans under other conditions (Hammond, 1990) including multiple actors (Suguwara, 1995) An actor can create a plan from scratch using a given set of STRIPS like operators (Fikes Nilsson, 1971) in a hierarchical planner (c.f. Sacerdoti, 1974; Knoblock, 1990) Depending on their type, di#erent actors have di#erent operations that they can perform. The special purpose operators for the actors in MOVERS WORLD are: lifter LIFT, LIFT TOGETHER, CARRY, CARRY TOGETHER, LOAD, LOAD TOGETHER, UNLOAD, UNLOAD TOGETHER, PUT DOWN, and PUT DOWN TOGETHER. ....

Knoblock, C. A. (1990). Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, pages 923--928.

.... previously satisfied prerequisites and its prerequisites might be already satisfied (the number of unsatisfied prerequisites decreases) Therefore regressing the mtc on the action schemas themselves is needed to predict some changes made by an action addition to the truth value of prerequisites [Knoblock 90] Smith Peot 93] Thus, the number of times the mtc must be called does not depend on the number of calls to GeneratePlan and not, in particular, to promotion, demotion, unification of separation. We use this property to add a condition on the evaluation of line 1 of GeneratePlan while ....

.... since depth first search is good for shallow solution paths in general [Langley 92] Many planners are propositional closed literals without any variables (SNLP, UCPOP) For example, abstractions based on time ordering have been shown to improve the efficiency of the search in general [Knoblock 90] But such propositional planners might perform arbitrarily poorly on particular individuals (see [Smith Peot 92] concerning propositional hierarchies and [Ginsberg 93] concerning propositional planning) Nevertheless, work of the late 70s showed that variables used as placeholders do improve ....

Craig Knoblock. Learning Abstraction Hierarchies for Problem Solving. In Proceedings of AAAI'90, Boston, MA, 1990.

....will use the routines to validate their own speedup learning experiments. z Affiliate Assistant Professor, Department of Biostatistics, University of Washington. 1 Motivation Speedup learning systems are systems that automatically generate search control knowledge (e.g. Etzioni, 1990b, Knoblock, 1990, Minton, 1988a, Mooney, 1989, O Rorke, 1989, Shavlik, 1990] The effectiveness of a speedup learning system is typically evaluated by comparing the performance of a problem solver, guided by the learned knowledge, with the performance of the problem solver given no control knowledge, or given ....

Knoblock, Craig A. 1990. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, Menlo Park, CA. AAAI Press.

....abstrips [Sac74] 1980 Plaisted Weak and Ordinary A. Pla80] 1986 Plaisted Generalisation A. Pla86] 1987 [Kor87] 1988 Tenenberg A. in Planning [Ten88] 1989 Giunchiglia Walsh Abstract T.P. GW90] 1990 Cremonini et al. A framework for A. CMS90] Pla90] Gamma Knoblock alpine [Kno90] 1991 Knoblock et al. A. for Planning [KTY91] KME91] 1992 Giunchiglia, Walsh T. of A. GW92] SP92] Gamma 1994 Bacchus et al. highpoint [BY94] 1995 Nakay, Levy Semantic T. of A. NL95] BJ95] Gamma 1996 Bundy et al. resistor prob. BGSW96] Giunchiglia et al. absfol [GV96] ....

C.A. Knoblock. Learning Abstraction Hierarchies for Problem Solving. In Proceedings AAAI-90, Boston, MA, 1990.

No context found.

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National ConferenceonArtificial Intelligence, pages 923--928, Boston, MA, 1990.

No context found.

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National ConferenceonArtificial Intelligence, pages 923--928, Boston, MA, 1990.

No context found.

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, pages 923--928, 1990.

....across levels since it requires that it is an inherent property of a problem space. While this constraint may be more restrictive than necessary, it provides a very effective heuristic for generating useful abstraction hierarchies. This constraint is captured by the ordered monotonicity property [31]. The ordered monotonicity property requires that every refinement of an abstract plan leaves the literals that comprise the abstract space unchanged. This property has two important features. First, it is computationally tractable to find abstraction hierarchies with this property from only the ....

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, Boston, MA, 1990, 923--928.

No context found.

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Thomas Dietterich and William Swartout, editors, Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Menlo Park, California, 1990. AAAI Press.

No context found.

Knoblock, C. A. 1990. Learning abstraction hierarchies for problem solving. In Dietterich, T., and Swartout, W., eds., Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90). Menlo Park, California: AAAI Press.

No context found.

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, pages 923--928, 1990.

No context found.

C. A. Knoblock. Learning abstraction hierarchies for problem solving. In Thomas Dietterich and William Swartout, editors, Proceedings of the 8th National Conference on Artificial Intelligence, Menlo Park, California, 1990. AAAI Press.

No context found.

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence, Los Altos, CA, 1990. Morgan Kaufmann.

No context found.

Craig A. Knoblock. Learning abstraction hierarchies for problem solving. In Thomas Dietterich and William Swartout, editors, Proceedings of the Eighth National Conference on Arti cial Intelligence (AAAI-90), Menlo Park, California, 1990. AAAI Press.

No context found.

Knoblock, C.A. Learning Abstraction Hierarchies for Problem Solving, Proc. of the 8th AAAI Conf., Menlo Park, CA, 1990, 923-928. 34

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC