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Exploiting Environment Configurability in Reinforcement Learning
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address complex control tasks. In a Markov Decision Process (MDP), the framework typically used, the environment is assumed to be a fixed entity that cannot be altered externally. There are, however, several real-world scenarios in which the environment can be modified to a limited extent. This book, Exploiting Environment Configurability in Reinforcement Learning, aims to formalize and study diverse aspects of environment configuration. In a traditional MDP, the agent perceives the state of the environment and performs actions. As a consequence, the environment transitions to a new state and generates a r...
Natural Language Processing and Information Systems
The workshop on Applications of Natural Language to Information Systems (NLDB)hassince1995providedaforumforacademicandindustrialresearchers and practitioners to discuss the application of natural language to both the development and use of software applications. Theuseofnaturallanguageinrelationtosoftwarehascontributedtoimpr- ing the development of software from the viewpoints of both the developers and the users. Developers bene?t from improvements in conceptual modeling, so- ware validation, natural language program speci?cations, and many other areas. Users bene?t from increased usability of applications through natural language query interfaces, semantic webs, text summarizations, etc. The integration of natural language and information systems has been a - search objective for a long time now. Today, the goal of good integration seems not so far-fetched. This is due mainly to the rapid progress of research in natural language and to the development of new and powerful technologies. The in- gration of natural language and information systems has become a convergent point towards which many researchers from several research areas are focussing.
Deceptive AI
This book constitutes selected papers presented at the First International Workshop on Deceptive AI, DeceptECAI 2020, held in conjunction with the 24th European Conference on Artificial Intelligence, ECAI 2020, in Santiago de Compostela, Spain, in August 2020, and Second International Workshop on Deceptive AI, DeceptAI 2021, held in conjunction with the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021, in Montreal, Canada, in August 2021. Due to the COVID-19 pandemic both conferences were held in a virtual mode. The 12 papers presented were thoroughly reviewed and selected from the 16 submissions. They present recent developments in the growing area of research in the interface between deception and AI.
Explainable Human-AI Interaction
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as ...
Basic Computers for Seniors and Beginners
All Windows Computer Tools, Built-in hidden Tools in Windows, Windows editions and Versions, Using the Windows Shell and commands. The correct way to Shutdown and startup windows using the built-in hidden tools. Protecting your Computer. Using the Free Antivirus Application. How to get Free Software. Cleaning utilities to keep the Computer registry clean and to Enhance Performance. Using MMC.
Introduction to Symbolic Plan and Goal Recognition
This is a high-level introduction and overview of plan and goal recognition including the core elements and practical advice for modeling them. Along with activity recognition, these areas of research play a crucial role in a wide variety of applications including assistive technology, software assistants, computer and network security, human-robot collaboration, natural language processing, video games, and much more. This synergistic area of research combines, unites, and makes use of techniques and research from a wide range of areas including user modeling, machine vision, automated planning, intelligent user interfaces, human-computer interaction, autonomous and multi-agent systems, nat...
Positive Unlabeled Learning
Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions. They do this by examining an enormous quantity of labeled training data to find patterns too complex for human observation. However, in many real-world applications, well-labeled data can be difficult, expensive, or even impossible to obtain. In some cases, such as when identifying rare objects like new archeological sites or secret enemy military facilities in satellite images, acquiring labels could require months of trained human observers at incredible expense. Other times, as when attempting to predict disease infection during a pandem...
Windows System and User Tools
Windows user Tools and hidden tools to manage windows computers, with new WiFi Standard with Hardware and Software tools to empower users to manage, troubleshoot and setup their own security.Special cleaning tools to keep the registry clean of junk and other malware, information on Basic specifications all users should know about Windows and all of its versions and types.The Shell and MMC commands for the users and how to correctly user these tools, and how to obtain more Performance from Windows.Complete Windows in Graphics format to better understand how it works, using the hidden tools embedded in Windows 10.
Applying Reinforcement Learning on Real-World Data with Practical Examples in Python
Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. It has shown human level performance on a number of tasks (REF) and the methodology for automation in robotics and self-driving cars (REF). This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant con...
Multi-Agent Reinforcement Learning
The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches. Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the fieldâ...
