Project Collective Intelligence
Collective intelligence, collective dynamics, simulation: microscopic vs macroscopic modeling, swarm robotics, agent-based simulation.
Learning objectives
The main objectives of this course are: 1. To understand the notion of collective intelligence and natural and artificial systems, and the difference between modeling them at the macroscopic versus the microscopic scale, (Knowledge and understanding) 2. To gain insight into open research issues related to collective intelligence and collective dynamics, (Knowledge and understanding) 3. To learn the use of simulators to conduct empirical research in this field. (Apply Knowledge and understanding) (Make judgements) (Lifelong learning skills)
The whole is greater than the sum of its parts. Or isn't it? This course is concerned with collective intelligence and collective dynamics. We will elaborate on the differences between modeling these systems at the microscopic versus the macroscopic scale, discuss the philosophical as well as the technical aspects of such modeling, and learn to conduct experiments with virtual agents that interact in virtual worlds. Collective Intelligence concepts will be also positioned and studied in relationship with Artificial Life and Swarm Robotics.
Assessment
Students will work in teams of three, four, or five (depending on total student numbers). They will subject to a compulsory evaluation every week that involves a presentation and demonstration with simulation videos. Answering questions during the group presentation and discussion is part of the assessment Grading is based on: 1) Quality of the presentation and degree of achievement of the task: a. Flocking: the agents need to move together in a group and in a common direction. This task is mainly meant as a formative training for the simulation environment. 0% of the final grade - only a formative grade will be given, assessed at the end of Week 1. b. Aggregation: the agents should aggregate all in one shelter among those available in the environment. 20% of the final grade, assessed at the end of Week 2. 15% will evaluate the group presentation and delivery, 5% will consist in individual evaluations where students will be asked individual questions, and the quality of the answer will be assessed. c. Collective dynamics simulation: A collective dynamics linked to a specific scenario will be given to students to simulate and study. Examples of this from past editions of the course are: covid spread simulation, prey/predator dynamics. This task runs for the final two weeks of the course. 40% of the final grade (the first 20% assessed at the end of Week 3 and the other 20% at the end of Week 4). 30% (15% each week) will evaluate the group presentation and delivery, 10% (5% each week) will consist in individual evaluations where students will be asked individual questions, and the quality of the answer will be assessed. 2) Short report of the approach and experimental results into 40% of the final grade. The report is written in a group, and a group grade will be given. Overall, the final grade is made up of the following component: 45% of the grade for group presentation performed in a controlled environment 15% of the grade for individual presentation (answers to questions) performed in a controlled environment 40% of the grade is group report The resit options are the following. In case one of the evaluations listed under point 1) is missed (b or a component of c), the students have the chance to submit a video containing a presentation and a live demo. In this case, the grade of the specific component (b, or a part of c) will be capped to 55% In case of late submission of the report, a penalty of 10% on the grade of the report component will be applied for each 24 hours of delay. A maximum of 5 days delay is allowed (corresponding to a penalty of 50% on the grade of the report component).
Teaching methods
The course combines literature study and moderated discussions with experimental work in computer simulations. Students will work in small groups.
Literature
Introductory papers on swarm robotics and collective dynamics, e.g. the review here https://link.springer.com/article/10.1007/s11721-012-0075-2
Prerequisites
Coding skills in Python are necessary.
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Project Collective Intelligence
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