Introduction to Symbolic Plan and Goal Recognition

This book PDF is perfect for those who love Computers genre, written by Reuth Reuth Mirsky and published by Springer Nature which was released on 31 May 2022 with total hardcover pages 100. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Introduction to Symbolic Plan and Goal Recognition books below.

Introduction to Symbolic Plan and Goal Recognition
Author : Reuth Reuth Mirsky
File Size : 52,9 Mb
Publisher : Springer Nature
Language : English
Release Date : 31 May 2022
ISBN : 9783031015892
Pages : 100 pages
Get Book

Introduction to Symbolic Plan and Goal Recognition by Reuth Reuth Mirsky Book PDF Summary

Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. 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, natural language understanding, and machine learning. It plays 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 many more. This wide range of applications and disciplines has produced a wealth of ideas, models, tools, and results in the recognition literature. However, it has also contributed to fragmentation in the field, with researchers publishing relevant results in a wide spectrum of journals and conferences. This book seeks to address this fragmentation by providing a high-level introduction and historical overview of the plan and goal recognition literature. It provides a description of the core elements that comprise these recognition problems and practical advice for modeling them. In particular, we define and distinguish the different recognition tasks. We formalize the major approaches to modeling these problems using a single motivating example. Finally, we describe a number of state-of-the-art systems and their extensions, future challenges, and some potential applications.

Introduction to Symbolic Plan and Goal Recognition

Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This synergistic area of research combines, unites, and makes use of techniques and research from a wide range of areas including user modeling, machine

Get Book
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

Get Book
Transfer Learning for Multiagent Reinforcement Learning Systems

Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is

Get Book
Network Embedding

heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.

Get Book
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,

Get Book
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. This book

Get Book
Plan  Activity  and Intent Recognition

Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of

Get Book
A Concise Introduction to Models and Methods for Automated Planning

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in

Get Book