The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Motivated Learning for Computational Intelligence
Abstract
This chapter describes a motivated learning (ML) method that advances model building and learning techniques required for intelligent systems. Motivated learning addresses critical limitations of reinforcement learning (RL), the more common approach to coordinating a machine’s interaction with an unknown environment. RL maximizes the external reward by approximating multidimensional value functions; however, it does not work well in dynamically changing environments. The ML method overcomes RL problems by triggering internal motivations, and creating abstract goals and internal reward systems to stimulate learning. The chapter addresses the important question of how to motivate an agent to learn and enhance its own complexity? A mechanism is presented that extends low-level sensory-motor interactions towards advanced perception and motor skills, resulting in the emergence of desired cognitive properties. ML is compared to RL using a rapidly changing environment in which the agent needs to manage its motivations as well as choose and implement goals in order to succeed.
Related Content
|
Muhammad Naeem, Salman Memon, Anita Larik, Syed Rizwan Mehdi, Hasan Ahmed Faridi, Khalida Khan, Sana Zafar, Manoj Kumar.
© 2026.
20 pages.
|
|
Imdad Ali Shah, N. Z. Jhanjhi.
© 2026.
12 pages.
|
|
Hafsa Muzammal, Muhammad Zaman, Muhammad Safdar, Muhammad Adnan Shahid, Zuhaib Nishtar, Muhammad Bilal, Muntaha Munir, Mehar Muhammad Haseeb, Aamir Raza, Syed Intsar Hussain Shah, Usman Zafar, Nalain E. Muhammad, Hafiz Muhammad Bilawal Akram.
© 2026.
30 pages.
|
|
Luminita Diaconu, Yassine Mouniane.
© 2026.
32 pages.
|
|
Kumar J. Parmar, Tejas Chandulal Chauhan, T. Premavathi.
© 2026.
32 pages.
|
|
Mahmoud Oudghiri, Mohamed El Bakkali, Yassine Mouniane, Nagla Abid, Samah Bouhassoun, Fatima-ezzahra Jaayefar, Fath Alah Elwahab, Issam El-Khadir, Ahmed Chriqui, Mohammed Ibriz.
© 2026.
26 pages.
|
|
Issam El-Khadir, Yassine Mouniane, Ahmed Chriqui, Mohamed El Bakkali, Driss Hmouni.
© 2026.
34 pages.
|
|
|