An Artificial Intelligence (AI) agent can perceive an environment through sensors and act in the environment through actuators. When performing tasks in a known environment, an agent knows what actions it can execute and how they affect the environment state, but when the environment is unknown, the agent needs to learn how the environment works to make good decisions for accomplishing tasks. In a real-world situation, an agent may have only low-level perceptions of the environment rather than the high-level representations required to make decisions by means of symbolic planning.
This book, Integrating Planning and Learning for Agents Acting in Unknown Environments proposes an architecture that integrates learning, planning, and acting. The author, Leonardo Lamanna, won the 2023 Marco Cadoli award, an annual award from the Italian Association for Artificial Intelligence (AIxIA) for the best doctoral thesis in the field of artificial intelligence, for this work. The approach combines data-driven learning methods for building an environment model with symbolic planning techniques for reasoning on the learned model, focusing on learning the model, either from continuous or symbolic observations. The problem of online learning the mapping between continuous perceptions and symbolic states is tackled, and symbolic planning techniques are exploited to enable an agent to autonomously gather relevant information online, which is required by learning methods to overcome some of the simplifying assumptions of symbolic planning. The effectiveness of the approach in simulated complex environments is shown experimentally and the applicability of the approach in real environments is demonstrated by conducting experiments on a real robot.
Outperforming state-of-the-art methods, the approach described in this book will be of interest to all those working in the field of AI and autonomous agents.
