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russel d boxes methodology black box control systems 2021

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Textbook in PDF format Preface References Acknowledgements Donald Michie: A Personal Appreciation Introduction Machine Intelligence Are Computers Intelligent? Can Computers Learn? The Robot and the Box Does the BOXES Algorithm Learn? Can Computers Think? Does the BOXES Algorithm Think? The Purpose of This Book A Road Map to Reading This Book Concluding Thoughts References Learning and Artificial Intelligence (AI) The Game Metaphor Computers Can Be Programmed to Play Games Playing by Rules Reactionary Strategy Games Noughts and Crosses OXO Not Noughts and Crosses Incentives and Learning Velocity Design of a Noughts and Crosses Engine Overview Software Substructures Typical Results Chance and Trial and Error Chance and the Community Chest Learning with Guesswork Random Initialization Positional Move Strengths The Payoff Matrix The Signature Table Rewards and Penalties Failure-Driven Learning Concluding Thoughts Reversi (Othello) The BOXES Method as a Game References Introduction to BOXES Matchboxes Components of the BOXES Method Defining the Game Board Identifying Game Situations Selecting Game Piece Actions Real-Time Data Handling Detecting an End Game Situation Updating the Signature Table Overall Performance Data Desired Level of Achievement Individual Box Decision Data Overall Software Design Concluding Comments References Dynamic Control as a Game Control of Dynamic Systems The Dynamic System Game Board State Variables and State Integers Creating a Unique System Integer Signature Table Control End of Game Action Actual Real-Time Data Collection Short Duration Mechanically Unstable Systems Continuous Systems with Sample Data Update Procedures Concluding Comments References The Trolley and Pole Control of a Simulated Inverted Pendulum Using the BOXES Method Introduction The Trolley and Pole Model The Trolley and Pole Signature Table Systems Engineering An Overall Performance Metric The Importance of State Boundaries Computation of a Unique System Integer Simulation Software The Campaign Setup Phase The Individual Run Phase Simulation Results Typical Results Update of Statistical Databases Determination of Decision Strength Near-Neighbor Advisor Cells Conclusions References The Liverpool Experiment Introduction to Reality The Liverpool Trolley and Pole Rig Practical Aspects of the Liverpool System Instrumentation and the State Variables Manual Auto-start Driving the Trolley The Microprocessor The BOXES Algorithm and the Real-Time Monitor Systems Engineering How Boundaries on Each State Variable Were Imposed Results from the Liverpool Rig Conclusions References Solving the Auto-Start Dilemma Introduction to the Auto-Start Dilemma Random Restart Simulation Software Restart Software Random Initial State Integers Automated Catch-Up Restart Method Catch-Up Restart in Simulated Systems Catch-Up Restart in a Real Trolley and Pole Rig Catch-Up Method Conclusion Systems Engineering Manual Experiments Conclusions References Other BOXES Applications Continuous System Control Continuous Control A Different Perspective on Failure Constructive Failure Limited Failure Creating a Learning Interlude Outcome-Based Performance Evaluation An Example Outcome-Based Approach Outcome-Based Assessment Training Continuous Automata BOXES Control of a Continuous System PID Control State Variable Control BOXES for Continuous Systems A BOXES Augmented Controller Results Outcome-Based Reward and Penalty Results Obtained for the Example Conclusions References Other On/Off Control Case Studies On/Off Control Learning Algorithms Run Time Data Signature Table Update Application Summary Fedbatch Fermentation The Fedbatch Fermentation Process A Fedbatch Fermentation Model BOXES Control of a Fedbatch Fermentor A Municipal Incinerator with BOXES Control A Model of a Municipal Incinerator BOXES Control of the Municipal Incinerator Reversing a Tractor Trailer Model of the Tractor Trailer BOXES Control of Tractor Trailer Reversal Conclusions References Two Nonlinear Applications Introduction Database Access Forecasting Application of BOXES to Disk Accessing Learning in the Adapted BOXES Algorithm Forecasting Simulation Results Conclusions Disk Accessing Stabilizing Lorentz Chaos Introduction BOXES and the Fractal Dimension Results for Lorenz Chaos Under BOXES Control Conclusions Lorentz Equations Conclusions References Extending the Algorithm Accelerated Learning Introduction Preset Fixed Signature Table Values Reduction of the Importance of Short Runs Collaboration Among Cells Playing Bridge Swarm and Ant Colony Systems Advisors in the BOXES Algorithm Locating Peer Advisor States Relocation of Boundaries Conclusions References Two Advising Paradigms Introduction Decision Strengths of Cells Identification and Ranking of Advisor Cells Advisor Strength Criteria Advising Schemas Advising by Voting Advising Using Cell Strengths Delayed Advising Advisor Accountability Conclusions References Evolutionary Studies Research Introduction State Boundary Experiments Variation in the Number and Size of State Zones Preliminary Conclusions An Evolutionary Paradigm Types of Zones An Evolutionary Method Interpreting Signature Table Statistics An Evolutionary Algorithm Example Results Conclusions References V Further Thoughts A Priori Knowledge What are Black Box Systems? An Intelligent Pacemaker Controlling a Steel Rolling Mill Why Use the Cart-and-Pole Exemplar? What does the BOXES Paradigm Need to Know? The Physical System to be Controlled The Division of the State Variables into Zones Other BOXES Configuration Data Items The Signature Table Evaluating System Merit In-Run Data Collection The Cell Usage Database Learning Parameters Fixed Cell Strategies Create Non-changeable States Strong Cell Freezing Can Fixed Cells icipate in Evolutionary Studies? Conclusions References Detecting and Handling Jitter Introduction Why Does Jittering Occur? How Jitter Affects Merit A Numerical Illustration How Jittering Corrupts Individual Cell Data Detection of Jittering in a BOXES System Possible Strategies for Jitter Remediation Executive Internal Action Jitter Proof Software Conclusions References Handling Untrained Data Glossary of Computational Terms A Mathematical BOXES Test Engine Scenario : Linear Increase or Decrease Scenario : Linear Saw Tooth Scenario : Complex or Random Pattern Forgetfulness Observations Using the BOXES Test Engine Aging Global Use (GU) Aging Global Life (GL) System Merit just Aging the Global Life (GL) System Merit for a More Complex Scenario Effect of Differing Values of Δk Summary of δk as a Learning Agent An Alternate Aging Strategy Using a FIFO Stack Architecture The FIFO Data Structure Application of the FIFO Stack to BOXES Merit Connection of the Test Engine to the FIFO Stack Merit Values Using the FIFO Stack Conclusions References Conclusions Summary and Conclusions Some Philosophical Commentary Bloom’s Taxonomy Introduction and I: Learning and Artificial Intelligence Introduction The Game Metaphor Introduction to BOXES Dynamic Control as a Game The Trolley and Pole Control of an Inverted Pendulum Using BOXES The Liverpool Experiment Solving the Auto-start Dilemma Other BOXES Applications Continuous System Control Other On/Off Control Case Studies Two Nonlinear Applications Improving the Algorithm Accelerated Learning Two Advising Paradigms Evolutionary Studies Research Further Thoughts A Priori Knowledge Detecting and Handling Jitter Handling Untrained Data Modifications to Standard BOXES Software Research Questions for Future Study Is There an Optimal Number of States? Is There a Generic Merit Formula? Is There a Generic Cell Strength Formula? Conclusions Some More Final Thoughts References Appendix A Glossary of Terms and Abbreviations Appendix B BOXES Software Notes QB: A Better Quickbasic Essentials of Simulation Software Housekeeping Auto-start Reset Inner Loop Variables Inner Loop Control Normalization of State Variables Calculation of System Integers Accessing the Signature Table Saving In-run Data Attaching the BOXES Controller State Equations of the System Model Numerical Integration Saving Global Data at the End of a Run Detecting and Avoiding Jitter Real-Time Software Conclusions Appendix C BOXES Publications, Lectures, and Presentations by the Author Author Biography Index

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