Special Topics in AI: Intelligent Agents and Multi-Agent Systems

2012/13

PhD Course

Lesson Schedule

  1. Wed. 17 Oct; Room M; 15:30 -- 17:30; Introduction
  2. Tue. 23 Oct; Room H; 15:30 -- 17:30; DCOPs -- Exact Approaches
  3. Tue. 30 Oct; Room H; 15:30 -- 17:30; Seminar: Theory and Practice of Coordination Algorithms exploiting the Generalised Distributive Law
    Speaker: Dr. Francesco Delle Fave, slides
  4. Mon. 5 Nov; Sala Verde; 16:00 -- 18:00 Seminar MADMASS: Massively Distributed Multi Agent System Simulator
    Speaker: Dr. Vittorio Amos Ziparo, slides
  5. Tue. 13 Nov; Room H; 15:30 -- 17:30; DCOPs -- Approximate Approaches
  6. Tue. 20 Nov; Room H; 15:30 -- 17:30; Market Based Task Allocation (Auctions)
  7. Tue. 4 Dec; Room H; 15:30 -- 17:30; Probabilistic approaches to robotics (State Estimation and Motion Planning)
  8. Tue. 11 Dec; Room H; 15:30 -- 17:30; Affinity Propagation and max-sum, Andrés Méndez
  9. Tue. 18 Dec; Room H; 15:30 -- 17:30; Reasoning about Knowledge in Multi-Agent Systems, Francesco Olivieri e Simone Scannapieco
  10. Thu. 24 Jan; Room L; 15:30 -- 16:30; Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces, Bogdan Maris
  11. Mon. 28 Jan; Meeting Room (II floor); 15:30 -- 16:30; Multi-armed bandit problem and its applications in reinforcement learning, Pietro Lovato
  12. Tue. 05 Feb; Room H; 15:30 -- 16:30; A tutorial on Particle filters for On-line Non-linear/Non-Gaussian Bayesian Tracking, Andrea Calanca

Course Descripton

This course will focus on AI based techniques and algorithms to build Intelligent Agents and Multi-Agent systems. The research area of Agents and Multi-Agent Systems focuses on building autonomous computational units that can perceive, plan, act and interact with others (agents or humans) in the environment where they live. This research area is wide and spans across many diverse fields such as Operative Research, Machine Learning, Game theory, Robotics, etc. The course will provide an introduction to agents and multi-agent systems and will then focus on specific topics that have recently gained increasing attention in this research community. More specifically the course will address: i) algorithms and techniques to perform optimization in the context of Multi-Agent Systems (with a specific focus on Distributed Constraint Optimization approaches); ii) techniques and methodologies to reason under uncertainty and over time (such as Markov Decision Processes); iii) Artificial Intelligence techniques for mobile robots and multi-robot systems (e.g., path planning, self-localization, exploration etc.).

Class Activity

In the first part of the course the instructor will offer a block of lectures to provide students with basic background knowledge on relevant issues, methodologies and techniques regarding the above topics. In the second part of the course students will be asked to read, present to the class, and discuss a set of selected papers. Presentation and class participation will form the basis for student evaluation.
 

Docente/Teacher: Alessandro Farinelli