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Lecture

Intelligent Agents: Making Decisions and Planning

In course

CS-430: Intelligent agents

Software agents are widely used to control physical, economic and financial processes. The course presents practical methods for implementing software agents and multi-agent systems, supported by programming exercises, and the theoretical underpinnings inc ...

Description

This lecture by the instructor introduces the concept of intelligent agents, focusing on making intelligent decisions and complex planning. The course covers topics such as machine learning, reinforcement learning, game theory, and building systems of multiple agents for applications like self-driving cars and intelligent control systems.

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Instructor

Boi Faltings

Professor Faltings joined EPFL in 1987 as professor of Artificial Intelligence. He holds a PhD degree from the University of Illinois at Urbana-Champaign, and a diploma from the ETHZ. His research has spanned different areas of intelligent systems linked to model-based reasoning. In particular, he has contributed to qualitative spatial reasoning, case-based reasoning (especially for design problems), constraint satisfaction for design and logistics problems, multi-agent systems, and intelligent user interfaces. His current work is oriented towards multi-agent systems and social computing, using concepts of game theory, constraint optimization and machine learning. In 1999, Professor Faltings co-founded Iconomic Systems, a company that developed a new agent-based paradigm for travel e-commerce. He has since co-founded 5 other startup companies and advised several others. Prof. Faltings has published more than 150 refereed papers on his work, and participates regularly in program committees of all major conferences in the field. He has served as associate editor of of the major journals, including the Journal of Artificial Intelligence Research (JAIR) and the Artificial Intelligence Journal. From 1996 to 1998, he served as head of the computer science department.

Official source

Related concepts (29)

Intelligent agent

In artificial intelligence, an intelligent agent (IA) is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

Deep reinforcement learning

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.

Game theory

Game theory is the study of mathematical models of strategic interactions among rational agents. It has applications in all fields of social science, as well as in logic, systems science and computer science. The concepts of game theory are used extensively in economics as well. The traditional methods of game theory addressed two-person zero-sum games, in which each participant's gains or losses are exactly balanced by the losses and gains of other participants.

Self-driving car

A self-driving car, also known as an autonomous car, driverless car, or robotic car (robo-car), is a car that is capable of traveling without human input. Self-driving cars use sensors to perceive their surroundings, such as optical and thermographic cameras, radar, lidar, ultrasound/sonar, GPS, odometry and inertial measurement units. Control systems interpret sensory information to create a three-dimensional model of the vehicle's surroundings.

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