A two stage approach for distributed cooperative manipulation of an unknown object without explicit communication and unknown number of robots

This thesis describes a symbolic execution system, PAN, that is able to symbolically execute loops. PAN achieves this by generalizing the effect of a few loop iterations to predict the effect of an unknown number of iterations. PAN operates on relatively unstructured loops that include 'go to' type constructs, allowing multiple exits from a loop. PAN uses a two stage generalization approach using techniques developed in Artificial Intelligence systems. The first stage uses models of expected loop effects and requires only limited search to generalize the effect of simple loops The second stage uses a less constrained approach that can generalize the effects of more complex loops by using extensive search. Fundamental to PAN's generalization method is the sequence. These are identified using models and used in both stages of the generalization process.

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Two-Stage Spline-Approximation with an Unknown Number of Elements in Applied Optimization Problem of a Special Kind

Mathematics and Statistics ◽

10.13189/ms.2021.090401 ◽

2021 ◽

Vol 9 (4) ◽

pp. 411-420

Author(s):

V. I. Struchenkov ◽

D. A. Karpov

Keyword(s):

Optimization Problem ◽

Spline Approximation ◽

Special Kind ◽

Two Stage ◽

Unknown Number ◽

Applied Optimization

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Analysis of Loop Programs Using Generalization

10.26686/wgtn.16945372.v1 ◽

2021 ◽

Author(s):

◽

Glenn Colman

Keyword(s):

Artificial Intelligence ◽

Symbolic Execution ◽

Two Stage ◽

Second Stage ◽

Unknown Number ◽

Extensive Search ◽

Number Of Iterations ◽

Artificial Intelligence Systems

This thesis describes a symbolic execution system, PAN, that is able to symbolically execute loops. PAN achieves this by generalizing the effect of a few loop iterations to predict the effect of an unknown number of iterations. PAN operates on relatively unstructured loops that include 'go to' type constructs, allowing multiple exits from a loop. PAN uses a two stage generalization approach using techniques developed in Artificial Intelligence systems. The first stage uses models of expected loop effects and requires only limited search to generalize the effect of simple loops The second stage uses a less constrained approach that can generalize the effects of more complex loops by using extensive search. Fundamental to PAN's generalization method is the sequence. These are identified using models and used in both stages of the generalization process.

Download Full-text

Analysis of Loop Programs Using Generalization

10.26686/wgtn.16945372 ◽

2021 ◽

Author(s):

◽

Glenn Colman

Keyword(s):

Artificial Intelligence ◽

Symbolic Execution ◽

Two Stage ◽

Second Stage ◽

Unknown Number ◽

Extensive Search ◽

Number Of Iterations ◽

Artificial Intelligence Systems

This thesis describes a symbolic execution system, PAN, that is able to symbolically execute loops. PAN achieves this by generalizing the effect of a few loop iterations to predict the effect of an unknown number of iterations. PAN operates on relatively unstructured loops that include 'go to' type constructs, allowing multiple exits from a loop. PAN uses a two stage generalization approach using techniques developed in Artificial Intelligence systems. The first stage uses models of expected loop effects and requires only limited search to generalize the effect of simple loops The second stage uses a less constrained approach that can generalize the effects of more complex loops by using extensive search. Fundamental to PAN's generalization method is the sequence. These are identified using models and used in both stages of the generalization process.

Download Full-text

Analysis of Loop Programs Using Generalization

10.26686/wgtn.16935034.v1 ◽

2021 ◽

Author(s):

◽

Glenn Colman

Keyword(s):

Artificial Intelligence ◽

Symbolic Execution ◽

Two Stage ◽

Second Stage ◽

Unknown Number ◽

Extensive Search ◽

Number Of Iterations ◽

Artificial Intelligence Systems

This thesis describes a symbolic execution system, PAN, that is able to symbolically execute loops. PAN achieves this by generalizing the effect of a few loop iterations to predict the effect of an unknown number of iterations. PAN operates on relatively unstructured loops that include 'go to' type constructs, allowing multiple exits from a loop. PAN uses a two stage generalization approach using techniques developed in Artificial Intelligence systems. The first stage uses models of expected loop effects and requires only limited search to generalize the effect of simple loops The second stage uses a less constrained approach that can generalize the effects of more complex loops by using extensive search. Fundamental to PAN's generalization method is the sequence. These are identified using models and used in both stages of the generalization process.

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Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

1989 ◽

Vol 47 ◽

pp. 368-369

Author(s):

Sengshiu Chung ◽

Peggy Cebe

Keyword(s):

Single Crystals ◽

High Performance ◽

Dilute Solution ◽

Two Stage ◽

Annealing Behavior ◽

High Performance Polymers ◽

Seeding Method ◽

Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

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