scholarly journals STACK with state

2017 ◽  
Vol 15 (2) ◽  
pp. 60 ◽  
Author(s):  
Matti Harjula ◽  
Jarmo Malinen ◽  
Antti Rasila
Keyword(s):  
Finite State Machine ◽  
State Machine ◽  
The State ◽  
Point Of View ◽  
State Variables ◽  
State Variable ◽  
Finite State ◽  
Input Event ◽  
Single Input ◽  
The Right

The question model of STACK provides an easy way for building automatically assessable questions with mathematical content, but it requires that the questions and their assessment logic depend only on the current input, given by the student at a single instant. However, the present STACK question model already has just the right form to be extended with state variables that would remove this limitation. In this article, we report our recent work on the state-variable extension for STACK, and we also discuss combining the use of state variables with our previous work on conditional output processing. As an outcome, we propose an expansion to the STACK question model, allowing the questions to act as state machines instead of pure functions of a single input event from the studentWe present a model question using the state variable extension of STACK that demonstrates some of the new possibilities that open up for the question author. This question is based on a finite state machine in its assessment logic, and it demonstrates aspects of strategic planning to solve problems of recursive nature. The model question also demonstrates how the state machine can interpret the solution path taken by the student, so as to dynamically modify the question behaviour and progress by, e.g., asking additional questions relevant to the path. We further explore the future possibilities from the point of view of learning strategic competencies in mathematics (Kilpatrick et al., 2001; Rasila et al., 2015).

scholarly journals FSM Encoding for BDD Representations

2007 ◽  
Vol 17 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Wilsin Gosti ◽  
Tiziano Villa ◽  
Alex Saldanha ◽  
Alberto Sangiovanni-Vincentelli
Keyword(s):  
Present State ◽  
Finite State Machine ◽  
Exact Algorithm ◽  
State Machine ◽  
The State ◽  
State Function ◽  
Output Function ◽  
State Variables ◽  
Finite State ◽  
Minimum Number

FSM Encoding for BDD RepresentationsWe address the problem of encoding the state variables of a finite state machine such that the BDD representing the next state function and the output function has the minimum number of nodes. We present an exact algorithm to solve this problem when only the present state variables are encoded. We provide results on MCNC benchmark circuits.

High-Speed and Area-Efficient Reconfigurable Multiplexer Bank for RAM-Based Finite State Machine Implementations

2015 ◽  
Vol 24 (07) ◽  
pp. 1550101 ◽  
Author(s):  
Raouf Senhadji-Navaro ◽  
Ignacio Garcia-Vargas
Keyword(s):  
High Speed ◽  
Finite State Machine ◽  
State Of The Art ◽  
Behavioral Model ◽  
State Machine ◽  
The State ◽  
Experimental Results ◽  
State Machines ◽  
Finite State ◽  
Area Efficient

This work is focused on the problem of designing efficient reconfigurable multiplexer banks for RAM-based implementations of reconfigurable state machines. We propose a new architecture (called combination-based reconfigurable multiplexer bank, CRMUX) that use multiplexers simpler than that of the state-of-the-art architecture (called variation-based reconfigurable multiplexer bank, VRMUX). The performance (in terms of speed, area and reconfiguration cost) of both architectures is compared. Experimental results from MCNC finite state machine (FSM) benchmarks show that CRMUX is faster and more area-efficient than VRMUX. The reconfiguration cost of both multiplexer banks is studied using a behavioral model of a reconfigurable state machine. The results show that the reconfiguration cost of CRMUX is lower than that of VRMUX in most cases.

Correction to Proof That Recurrent Neural Networks Can Robustly Recognize Only Regular Languages

1998 ◽  
Vol 10 (5) ◽  
pp. 1067-1069 ◽  
Author(s):  
Mike Casey
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Recurrent Neural Networks ◽  
Finite State Machine ◽  
State Machine ◽  
Regular Languages ◽  
State Variables ◽  
Finite Dimensional ◽  
Corrected Proof ◽  
Finite State

Our earlier article, “The Dynamics of Discrete-Time Computation, with Application to Recurrent Neural Networks and Finite State Machine Extraction” (Casey, 1996), contains a corollary that shows that finite dimensional recurrent neural networks with noise in their state variables that perform algorithmic computations can perform only finite state machine computations. The proof of the corollary is technically incorrect. The problem resulted from the fact that the proof of the theorem on which the corollary is based was more general than the statement of the theorem, and it was the contents of the proof rather than the statement that were used to prove the corollary. In this note, we state the theorem in the necessary generality and then give the corrected proof of the corollary.

MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

2018 ◽  
pp. 29-35
Author(s):  
N. V. Brovka ◽  
P. P. Dyachuk ◽  
M. V. Noskov ◽  
I. P. Peregudova
Keyword(s):  
Mathematical Model ◽  
Finite State Machine ◽  
Active Agent ◽  
Adaptive Testing ◽  
State Machine ◽  
Learning Activities ◽  
Independent Learning ◽  
Complex Object ◽  
Total Reward ◽  
Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

PENERAPAN FINITE STATE MACHINE UNTUK PENGENDALIAN ANIMASI PADA VIDEO GAME RPG NUSANTARA LEGACY

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Mustofa Mustofa ◽  
Sidiq Sidiq ◽  
Eva Rahmawati
Keyword(s):  
Life Cycle ◽  
Video Game ◽  
Finite State Machine ◽  
Role Playing ◽  
State Machine ◽  
Game Development ◽  
Development Life Cycle ◽  
Finite State ◽  
Role Playing Game

Perkembangan dunia yang dinamis mendorong percepatan perkembangan teknologi dan informasi. Dengan dorongan tersebut komputer yang dulunya dibuat hanya untuk membantu pekerjaan manusia sekarang berkembang menjadi sarana hiburan, permainan, komunikasi dan lain sebagainya. Dalam sektor hiburan salah satu industri yang sedang menjadi pusat perhatian adalah industri video game. Begitu banyaknya produk video game asing yang masuk ke dalam negeri ini memberikan tantangan kepada bangsa ini. Tentunya video game asing yang masuk ke negara ini membawa banyak unsur kebudayaan negara lain. Ini semakin membuat kebudayaan nusantara semakin tergeserkan dengan serangan kebudayaan asing melalui berbagai media. Maka dari itu peneliti mencoba untuk menerapkan Finite State Machine dalam merancang sebuah video game RPG (Role-Playing game) yang memperkenalkan kebudayaan. Dalam perancangan video game ini peneliti menggunakan metode GDLC(Game Development Life Cycle) agar penelitian ini berjalan secara sistematis. Dalam suatu perancangan video game tedapat banyak elemen, pada penelitian ini penulis lebih fokus pada pengendalian animasi karakter yang dimainkan pada video game ini. Dari perancangan yang dilakukan, disimpulkan bahwa Finite State Machine dapat digunakan untuk pengendalian animasi yang baik pada video game RPG. Diharapkan video game ini dapat menjadi salah satu media untuk mengenalkan kebudayaan nusantara

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

User permission isolation model based on finite state machine

2013 ◽  
Vol 33 (1) ◽  
pp. 149-152
Author(s):  
Jianjun LI ◽  
Yixiang JIANG ◽  
Jie QIAN ◽  
Wei LI ◽  
Yu LI
Keyword(s):  
Finite State Machine ◽  
State Machine ◽  
Model Based ◽  
Finite State
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