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The attached proceedings resulted from papers accepted for the 10th Annual GIFT Users Symposium held virtually on 24-25 May 2022. The first GIFT Users Symposium was held in 2013 with the goal to capture successful implementations of GIFT from the user community and to share recommendations leading to more useful capabilities for authors, researchers, and learners of Adaptive Instructional Systems (AISs). We are proud to publish our 10th edition of the proceedings, which provides an excellent collection of contributions covering all aspects of (AIS), with special attention towards future training and education concepts centered around competency-based learning, multi-modal methods, collaboration and team dynamics.
Current Issues in Computer Simulation is a collection of papers dealing with computer simulation languages, statistical aspects of simulation, linkage with optimization and analytical models, as well as theory and application of simulation methodology. Some papers explain the General Purpose Simulation System (GPSS), a programming package incorporating a language to simulate discrete systems; and the SIMSCRIPT, a general-purpose simulation language using English commands, for example, FORTRAN. Another simulation language is the General Activity Simulation Program (GASP), providing for an organizational structure to build models to simulate the dynamic performance of systems on a digital computer. Other papers discuss simulation models of real systems, including corporate simulation models, multistage consumer choice process, determination of maximum occupancy for hospital facilities, and the juvenile court system. Many computer simulations are statistical sampling experiments performed on a model of the system under investigation. Other papers discuss some of the variables involved in the statistical design and analysis of simulation experiments such as variance reduction techniques, generation of random variates, and experimental layout. For example, one application simulates inventory systems when many items are stocked in various locations. The collection is suitable for programmers, computer engineers, businessmen, hospital administrators, schools officials, and depositories of huge volumes of information or data.
This book is a resource for those who are new to intelligent tutoring systems (ITSs), as well as those with a great deal of experience with them. This is the tenth book in our Design Recommendations for Intelligent Tutoring Systems book series. The focus of this book is on Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analyses of varying components of ITSs. Each chapter in the book represents a different topic area, and includes a SWOT analysis that is specific to that topic and how it relates to ITSs. This book can be read in order, or a reader can choose a specific topic area and move directly to that chapter. Each SWOT Analysis describes the current state of the topic area, and how the lessons learned from the analysis could be applied to the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare et al., 2012; Sottilare et al., 2017). GIFT is an ITS architecture that is open-source, modular, and domain independent (Sottilare et al., 2017). Each book in the design recommendations series has addressed a different ITS topic area, and how the work in each chapter can relate to and inform the GIFT architecture. GIFT has continually been in development, with features consistently being added to improve functionality, as well as reduce the skill requirement for authoring content in GIFT. GIFT is freely available in both downloadable and Cloud versions at https://www.GIFTtutoring.org.
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
First multi-year cumulation covers six years: 1965-70.