Explainable Human-AI Interaction by Sarath Sreedharan
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 well as the human's model of the AI agent's task and goal models.-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
Sarath Sreedharan is a Ph.D. student at Arizona State University working with Prof. Subbarao Kambhampati. His primary research interests lie in the area of human-aware and explainable AI, with a focus on sequential decision-making problems. Sarath’s research has been featured in various premier research conferences, including IJCAI, AAAI, AAMAS,ICAPS, ICRA, IROS, etc., and journals like AIJ. He was also the recipient of Outstanding Program Committee Member Award at AAAI-2020.Anagha Kulkarni is an AI Research Scientist at Invitae. Before that, she received her Ph.D. in Computer Science from Arizona State University. Her Ph.D. thesis was in the area of human-aware AI and automated planning. Anagha’s research has been featured in various premier conferences like AAAI,IJCAI, ICAPS, AAMAS, ICRA, and IROS.Subbarao Kambhampati is a professor in the School of Computing & AI at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems. He is a fellow of the Association for the Advancement of Artificial Intelligence, the American Association for the Advancement of Science, and the Association for Computing Machinery, and was an NSF Young Investigator. He was the president of the Association for the Advancement of Artificial Intelligence, trustee of the International Joint Conference on Artificial Intelligence, and a founding board member of Partnership on AI. Kambhampati’s research, as well as his views on the progress and societal impacts of AI, have been featured in multiple national and international media outlets.
| SKU | Unavailable |
| ISBN 13 | 9783031037573 |
| ISBN 10 | 303103757X |
| Title | Explainable Human-AI Interaction |
| Author | Sarath Sreedharan |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2022-01-27 |
| Number of pages | 164 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |