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Linden, Theodore A.

Publications and source records attributed to Linden, Theodore A..

Generation and exploration of aggregation abstractions for scheduling and resource allocation

This paper presents research on the abstraction of computational theories for scheduling and resource allocation. The paper describes both theory and methods for the automated generation of aggregation abstractions and approximations in which detailed resource allocation constraints are replaced by constraints between aggregate demand and capacity. The interaction of aggregation abstraction generation with the more thoroughly investigated abstractions of weakening operator preconditions is briefly discussed. The purpose of generating abstract theories for aggregated demand and resources includes: answering queries about aggregate properties, such as gross feasibility; reducing computational costs by using the solution of aggregate problems to guide the solution of detailed problems; facilitating reformulating theories to approximate problems for which there are efficient problem-solving methods; and reducing computational costs of scheduling by providing more opportunities for variable and value-ordering heuristics to be effective. Experiments are being developed to characterize the properties of aggregations that make them cost effective. Both abstract and concrete theories are represented in a variant of first-order predicate calculus, which is a parameterized multi-sorted logic that facilitates specification of large problems. A particular problem is conceptually represented as a set of ground sentences that is consistent with a quantified theory.

Lowry, Michael R.

Decision theory for computing variable and value ordering decisions for scheduling problems

Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.

Linden, Theodore A.

JIGSAW: Preference-directed, co-operative scheduling

Techniques that enable humans and machines to cooperate in the solution of complex scheduling problems have evolved out of work on the daily allocation and scheduling of Tactical Air Force resources. A generalized, formal model of these applied techniques is being developed. It is called JIGSAW by analogy with the multi-agent, constructive process used when solving jigsaw puzzles. JIGSAW begins from this analogy and extends it by propagating local preferences into global statistics that dynamically influence the value and variable ordering decisions. The statistical projections also apply to abstract resources and time periods--allowing more opportunities to find a successful variable ordering by reserving abstract resources and deferring the choice of a specific resource or time period.

Linden, Theodore A.

Generation and Exploitation of Aggregation Abstractions for Scheduling and Resource Allocation

Our research is investigating abstraction of computational theories for scheduling and resource allocation. These theories are represented in a variant of first order predicate calculus, parameterized multisorted logic, that facilitates specification of large problems. A particular problem is conceptually stated as a set of ground sentences that are consistent with a quantified theory. We are mainly investigating the automated generation of aggregation abstractions and approximations in which detailed resource allocation constraints are replaced by constraints between aggregate demand and capacity. We are also investigating the interaction of aggregation abstractions with the more thoroughly investigated abstractions of weakening operator preconditions. The purpose of the theories for aggregated demand/capacity is threefold: first, to answer queries about aggregate properties, such as gross feasibility; second, to reduce computational costs by using the solution of aggregate problems to guide the solution of detailed problems; and third, to facilitate reformulating theories to approximate problems for which there are efficient problem solving methods. We also describe novel methods for exploiting aggregation abstractions.

Linden, Theodore A.