This book explores the subject of artificial psychology and how the field must adapt human neuro-psychological testing techniques to provide adequate cognitive testing of advanced artificial intelligence systems. It shows how classical testing methods will reveal nothing about the cognitive nature of the systems and whether they are learning, reasoning, and evolving correctly; for these systems, the authors outline how testing techniques similar to/adapted from human psychological testing must be adopted, particularly in understanding how the system reacts to failure or relearning something it has learned incorrectly or inferred incorrectly. The authors provide insights into future architectures/capabilities that artificial cognitive systems will possess and how we can evaluate how well they are functioning. It discusses at length the notion of human/AI communication and collaboration and explores such topics as knowledge development, knowledge modeling and ambiguity management, artificial cognition and self-evolution of learning, artificial brain components and cognitive architecture, and artificial psychological modeling.

Explores the concepts of Artificial Psychology and Artificial Neuroscience as applied to advanced artificially cognitive systems;
Provides insight into the world of cognitive architectures and biologically-based computing designs which will mimic human brain functionality in artificial intelligent systems of the future;
Provides description and design of artificial psychological modeling to provide insight into how advanced artificial intelligent systems are learning and evolving;
Explores artificial reasoning and inference architectures and the types of modeling and testing that will be required to "trust" an autonomous artificial intelligent systems.

Explores the concepts of Artificial Psychology and Artificial Neuroscience as applied to advanced artificially cognitive systems Provides insight into the world of cognitive architectures and biologically-based computing designs which will mimic human brain functionality in artificial intelligent systems of the future Provides description and design of artificial psychological modeling to provide insight into how advanced artificial intelligent systems are learning and evolving Explores artificial reasoning and inference architectures and the types of modeling and testing that will be required to "trust" an autonomous artificial intelligent systems

Autorentext

Dr. James A. Crowder currently serves as a Systems Fellow for Colorado Engineering Inc. and Subject Matter Expert (SME) in Autonomous Systems, Artificial Intelligence, and Systems Architecture. He holds a BS in Electrical Engineering, an MS in Electrical Engineering in Signal Processing, an MS in Applied Mathematics, and a PhD in Electrical Engineering and Applied Mathematics. Dr. Crowder has several patents pending in Artificial Intelligence and has over 100 published, peer-reviewed papers. Recent book publishing efforts with Springer Scientific books include: Artificial Cognition Architectures, Systems Engineering, Agile Design Methodologies, and Agile Project Management: Managing for Success, as well as chapters in several books on Big Data, Biomedical Engineering, and Cyber Physical Systems. His professional efforts include serving as a technical advisor and mentor to a STEM school in Douglas Country, Colorado, the Alexandria School of Innovation, as well as a technical reviewer for the Journal of Supercomputing and the Journal of Systemics, Cybernetics, and Informatics. Dr. Crowder has been interviewed, and articles written about his work in Artificial Intelligence, by Popular Science, Defense One, the Washington Post, Discovery News, and has written an article for TechCrunch that was published in June, 2016.

Dr. John N. Carbone, an Engineering Fellow, has served Raytheon Company and the defense industry for ~28 years. Dr. Carbone holds a BS in Computer Science, an MS in Engineering, MS equivalent in Software and Systems Engineering, and a PhD in Mechanical Engineering. Dr. Carbone has several national and international software patents and many peer-reviewed publications. Recent book publishing efforts for Springer Scientific books include topics on Applied Cyber Physical Systems, Biomedical Engineering, Artificial Cognition Architectures and Multi-Disciplinary Systems Engineering. Dr. Carbone's professional efforts include Computer Science board membership and curriculum advisor at Baylor University, Texas Tech University, and Texas A&M Commerce, as well as board membership for the indexed Journal of Integrated Design and Process Science (JIDPS), passionate STEM Facilitator, ACM Member and long-standing member of the Society of Design and Process Science (SDPS).

Dr. Shelli Friess is a Licensed Professional Counselor in Colorado, a National Certified Counselor, and an Approved Clinical Supervisor. Most of her experience in counseling has been with survivors of trauma, including domestic violence and sexual assault. She has over 18 years of experience as a counselor, clinical supervisor, and program director. She has also worked with offenders of domestic violence on parenting skills. In addition, she worked with children in elementary and middle school and has experience working with children in residential treatment. Dr. Friess is a core faculty at Walden University in the School of Counseling, within the College of Social and Behavioral Sciences. Her history includes earning her BA from University of Colorado with a major in psychology. She earned her MA from the University of Northern Colorado in community counseling with an emphasis in marriage and family systems. In addition, she earned her Ed.D. from Argosy University in counselor education and supervision. Dr. Friess has published several peer reviewed papers on Artificial Intelligence. Recent book publishing efforts with Springer Scientific books include: Artificial Cognition Architectures, Systems Engineering, Agile Design Methodologies, and Agile Project Management: Managing for Success."

Inhalt
Chapter 1. Introduction: Psychology and Technology.- Chapter 2. Systems-Level Thinking for Artificial Intelligent Systems.- Chapter 3. Psychological Constructs for AI Systems: The Information Continuum.- Chapter 4. Human-AI Collaboration.- Chapter 5. Abductive Artificial Intelligence Learning Models.- Chapter 6. Artificial Creativity and Self-Evolution: Abductive Reasoning in Artificial Life Forms.- Chapter 7. Artificial Intelligent Inferences utilizing Occam Abduction.- Chapter 8. Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems.- Chapter 9. Ontology-Based Knowledge Management for Artificial Intelligent Systems.- Chapter 10. Cognitive Control of Self-Evolving Life Forms (SELF) utilizing Artificial Procedural Memories.- Chapter 11. Methodologies for Continuous, Life-Long Machine Learning for AI Systems.- Chapter 12. Implicit Learning in Artificial Intelligence.- Chapter 13. Data Analytics: The Big Data Analytics Process (BDAP) Architecture.- Chapter 14.Conclusions and Next Steps.