Behavioral programming of autonomous characters based on probabilistic automata and personality

2004 ◽  
Vol 15 (34) ◽  
pp. 319-326 ◽  
Author(s):  
Luca Chittaro ◽  
Milena Serra
Keyword(s):  
Probabilistic Automata ◽  
Autonomous Characters ◽  
Behavioral Programming

scholarly journals Lower bounds for the state complexity of probabilistic languages and the language of prime numbers

2020 ◽  
Vol 30 (1) ◽  
pp. 175-192
Author(s):  
NathanaËl Fijalkow
Keyword(s):  
Lower Bound ◽  
Lower Bounds ◽  
Prime Numbers ◽  
Regular Languages ◽  
Automata Theory ◽  
Seminal Paper ◽  
Probabilistic Automata ◽  
State Complexity ◽  
Probabilistic Languages ◽  
Alternating Automata

Abstract This paper studies the complexity of languages of finite words using automata theory. To go beyond the class of regular languages, we consider infinite automata and the notion of state complexity defined by Karp. Motivated by the seminal paper of Rabin from 1963 introducing probabilistic automata, we study the (deterministic) state complexity of probabilistic languages and prove that probabilistic languages can have arbitrarily high deterministic state complexity. We then look at alternating automata as introduced by Chandra, Kozen and Stockmeyer: such machines run independent computations on the word and gather their answers through boolean combinations. We devise a lower bound technique relying on boundedly generated lattices of languages, and give two applications of this technique. The first is a hierarchy theorem, stating that there are languages of arbitrarily high polynomial alternating state complexity, and the second is a linear lower bound on the alternating state complexity of the prime numbers written in binary. This second result strengthens a result of Hartmanis and Shank from 1968, which implies an exponentially worse lower bound for the same model.

A Computer Tutorial on Consequences in Operant Learning

1997 ◽  
Vol 24 (3) ◽  
pp. 216-217 ◽  
Author(s):  
Robert B. Graham
Keyword(s):  
Correct Answer ◽  
Operant Learning ◽  
Study Guide ◽  
Questionnaire Data ◽  
General Psychology ◽  
Student Records ◽  
Student Reaction ◽  
Computer Tutorial ◽  
Behavioral Programming ◽  
Course Credit

This article describes a computer tutorial that teaches the fundamentals of consequences and contingencies in operant teaming. The tutorial content is appropriate for courses in general psychology, learning, and behavioral programming. Applications to animal and human situations are emphasized. The software repeats questions until the student is able to provide the correct answer, but spaces its repetitions to maximize retention. It saves student records as a basis for assignment of course credit. Student reaction to this form of presentation was very favorable. Questionnaire data showed that the students perceived the tutor as more useful in preparing for a test than a text or study guide would have been.

Programming in Restrictive Housing: Considerations for Improving Outcome Evaluations

2018 ◽  
Vol 45 (8) ◽  
pp. 1174-1191 ◽  
Author(s):  
H. Daniel Butler ◽  
Starr Solomon ◽  
Ryan Spohn
Keyword(s):  
Treatment Effect ◽  
Successful Implementation ◽  
Inmate Misconduct ◽  
The Past ◽  
What Works ◽  
Different Types ◽  
Time Served ◽  
Using Data ◽  
Outcome Evaluations ◽  
Behavioral Programming

A number of studies have identified “what works” in regard to the successful implementation of correctional programming over the past several decades. Few studies, however, have examined the complexities associated with programming in restrictive housing. Using data from a Midwestern department of corrections, we examined whether the provision of programming in restrictive housing achieved desired outcomes (e.g., reductions in inmate misconduct). The findings revealed the amount of time served in restrictive housing and confinement in different types of restrictive housing may influence estimations of a treatment effect. As a growing number of states seek to reform the use of restrictive housing, the proper implementation of cognitive-behavioral programming may increase institutional security and safety.

scholarly journals Bisimulations Meet PCTL Equivalences for Probabilistic Automata

2013 ◽  
Vol 9 (2) ◽  
Author(s):  
Lei Song ◽  
Lijun Zhang ◽  
Jens Godskesen ◽  
Flemming Nielson
Keyword(s):  
Probabilistic Automata

scholarly journals Probabilistic automata

1963 ◽  
Vol 6 (3) ◽  
pp. 230-245 ◽  
Author(s):  
Michael O. Rabin
Keyword(s):  
Probabilistic Automata

AN INVESTIGATION OF THE PHASE TRANSITIONS OF A FAMILY OF PROBABILISTIC AUTOMATA

2004 ◽  
Vol 07 (01) ◽  
pp. 93-123
Author(s):  
HEINZ MÜHLENBEIN ◽  
THOMAS AUS DER FÜNTEN
Keyword(s):  
Phase Transitions ◽  
Stationary Distribution ◽  
Classification Scheme ◽  
Mean Field ◽  
Finite Size ◽  
Order Parameters ◽  
Finite Size Scaling ◽  
Probabilistic Cellular Automata ◽  
Probabilistic Automata ◽  
Major Transitions

We investigate a family of totalistic probabilistic cellular automata (PCA) which depend on three parameters. For the uniform random neighborhood and for the symmetric 1D PCA the exact stationary distribution is computed for all finite n. This result is used to evaluate approximations (uni-variate and bi-variate marginals). It is proven that the uni-variate approximation (also called mean-field) is exact for the uniform random neighborhood PCA. The exact results and the approximations are used to investigate phase transitions. We compare the results of two order parameters, the uni-variate marginal and the normalized entropy. Sometimes different transitions are indicated by the Ehrenfest classification scheme. This result shows the limitations of using just one or two order parameters for detecting and classifying major transitions of the stationary distribution. Furthermore, finite size scaling is investigated. We show that extrapolations to n=∞ from numerical calculations of finite n can be misleading in difficult parameter regions. Here, exact analytical estimates are necessary.

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