Syllabus, Dept. Electrical and Computer Engineering, Ohio State University
ECE 7858 Intelligent Control (Cooperative Systems for Social Justice)
Enrolled students, see Carmen for more information.
Submit all assignments/projects electronically via Carmen (the LaTeX word processing system is preferred)
The syllabus below may change. It has evolved over the years, and continues to evolve, as it is a course to support cutting-edge research.
Homework problems below (except Homework #1), will change; check with Carmen before attempting a solution.
Instructor: Prof. Kevin Passino 416 Dreese Laboratory, passino.1@osu.edu
Office Hours: Set an appointment via email, or talk to me before or after class.
Scheduling: This course is offered in Spring semester of even-numbered years.
Prerequisites: Graduate standing is required by the numbering of the course per OSU policy; ECE 6754 Nonlinear Control Systems is useful but not required; ECE 5759 Optimization is useful but not required
Course Objectives: Social cooperative group systems modeling and anlaysis via gaining an understanding of modeling and qualitative analysis of networked and distributed dynamical systems, especially collective motion, agreement, choice, allocation, and others. Sociality and cooperation ideas permeate the treatment. Analysis methods include Lyapunov stability analysis and Matlab simulations.
Topical Outline:
Background for Analysis and Learning Mindset:
Homework 1:
(1) Establish background in stability analysis of dynamical systems. Read the introduction to stability paper by Michel posted at Carmen. This material is not covered in class; you build your background in this area via this assignment. This material is foundational for all topics covered in class. Prove that three different dynamical systems possess qualitative properties, where you choose the dynamical systems and analysis approaches based on the following constraints: (i) one system must be continuous time, one discrete time, and another of your choice; (ii) dimensionality: n=1 for one (scalar) system, n>1 and fixed for one system, and n arbitrary for one system; (iii) one system may be linear, but the other two must be nonlinear; (iv) prove that an invariant set (that you find) for the system, one with more than one point in it, is asymptotically stable (globally), that an invariant set (that you define) is exponentially stable (at least locally), and that one is uniformly ultimately bounded. State all your assumptions, and show all the steps of your three proofs. You may use any source aside from another student in the class, but reference your sources.
(2) Read the slides: "Successful Learning: Mindset and Metacognition" and write a one-page "reaction paper" (e.g., what you think about the ideas, whether they are useful to you in research or not: be specific). Do not dismiss this as unimportant relative to the mathematical part of the class.
Optional: Read the paper, AN Michel, K Wang, KM Passino, "Qualitative Equivalence of Dynamical Systems with Applications to Discrete Event Systems," Proc. 31st Conf. on Decision and Control, Tuscon, AZ, Dec. 1992, can some additional information can be found in Anthony N Michel and Kaining Wang, Qualitative Theory of Dynamical Systems: The Role of Stability Preserving Mappings, Marcel Dekker, NY, 1995. This covers general dyanmical systems on a metric space, qualitative properties, stability preserving mappings, and comparison theory. It is useful in generalizing the ideas covered in this course, that is, in research.
Introduction: Group/Community Dynamics and Cooperation (the basics; 2 weeks):
Class:
(1) Overview of challenges, feedback control, performance/robustness; social justice goals (e.g., diversity, equity, inclusion, common good, rights) and their quantification and guidance (e.g., for the design process). Groups, social groups, communications, examples (engineering design teams, communities), feedback control examples. Distributed dynamical systems, agents and communication topology. Sociotechnological systems, in design and real-time operation via human interfaces to extend human capability. Overview of mathematical and computational analysis methods. Complex system dynamics, stability, and the characterization of "emergence." Discuss paths to implementation: (i) develop strategies (rules) for humans to use; (ii) add technological advisors for processing information and aiding in decisions.
(2) Cooperation: (i) examples from behavioral economics and game theory, (ii) the prisoner's dilemma, direct (mutualism) and indirect reciprocity, and other mechanisms of cooperation; (iii) evolution of cooperation (e.g., tit-for-tat) using Axelrod's approach; and (iv) examples in humans, other species.
(3) Relevant fields: (i) Intelligent control methods including fuzzy control, neural networks, planning systems, attentional systems, learning, evolution, foraging. (ii) social psychology; and (iii) role theory.
Homework 2: Simulation of the evolution of cooperation using Axelrod's approach. You choose the parameters, write Matlab code, provide results (you decide on the form), explain each result carefully in terms of what was discussed in class.
Group/Community Agreement (plus Motion and Syncrhonization) (reaching a consensus; 1 week): Collective/coordinated motion, modeling and stability analysis of swarms (ODE based), distributed agreement/choice and distributed synchronization, human preference/opinion (cognitive variables) vector allignment in human social processes.
Class:
(1) Movies of flocks, schools, and swarms. See also, Schultz K.M., Passino K.M., Seeley T.D., "The Mechanism of Flight Guidance in Honeybee Swarms: Subtle Guides or Streaker Bees?," Journal of Experimental Biology, Vol. 211, pp. 3287-3295, 2008. (ii) Modeling and stability analysis of swarms (ODE based) via the paper: Yanfei Liu and Kevin M. Passino, “Stable Social Foraging Swarms in a Noisy Environment,” IEEE Trans. on Automatic Control, Vol. 49, No. 1, pp. 30-44, Jan. 2004 covering uniform boundedness, uniform ultimate boundedness, identical agent case, trajectory-following case, simulations (group/individual profile tracking in presence of noise).
(2) Relations to foraging theory. Effects of diversity. Relations to group formation and group cohesiveness.
(3) Application to distributed agreement: community elections problem formulation (candidate positions on topics, voter preferences/positions on topics, liberals/conservatives/moderates, inter-voter persuasion, etc.), discuss model and analysis of voter behavior and candidate choice. Relations to a model of democracy from Humanitarian Engineering: Advancing Technology for Sustainable Development. Dynamics of inclusion.
(4) Overview group size design Andrews B.W., Passino K.M., Waite T.A., "Social Foraging Theory for Robust Multiagent System Design," IEEE Trans. Automation Science and Engineering, Vol. 4, No. 1, pp. 79-86, pp. 79-86, Jan. 2007. (Featured in Current Anthropology, Vol. 49, No. 1, Feb. 2008.)
Homework 3: (i) Swarm simulation for general case (all features, for Liu/Passino "noisy" paper above) for N=2 and N=10 cases. Vary all parameters individually to illustrate impacts on swarm behavior (e.g., increase repulsion gain and show it will keep agents farther apart, generally). More information to follow. (ii) Simulation of political elections problem (cognitive variables case), case of two cogntitive variables, n=2 (e.g., "positions" on two different policies), N=10 voters, choice of nonlinear interactions, adjustment of parameters, and effects on candidate choice for the case of two candidates.
Optional: (i) Overview of concepts from other papers on swarms here (just search on the word "swarm" to see which papers); (ii) Liu Yanfei, Passino Kevin M., "Cohesive Behaviors of Multiagent Systems with Information Flow Constraints", IEEE Trans. on Automatic Control, Vol. 51, No. 11, pp. 1734-1748, Nov. 2006, (iii) application to distributed syncrhonization, (iii) discussion on impacts on modeling and analysis for applications. Discussions on mechanisms of cooperation, information flow, and resulting emergent types of cooperation that depend on the goal. Relations to a model of democracy from Humanitarian Engineering: Advancing Technology for Sustainable Development
Group/Community Choice (search/evaluation/selection and choosing the best of N, N unknown; 1 week): Nest-site selection by honeybees, "swarm cognition," bias removal by the group compared to the individual(?), human case.
Class: (i) Passino K.M., Seeley T.D., "Modeling and Analysis of Nest-Site Selection by Honey Bee Swarms: The Speed and Accuracy Trade-off", Behavioral Ecology and Sociobiology, Vol. 59, No. 3, pp. 427-442, Jan. 2006. Social justice goals and multicriterion decision making: Section 4.5 in Humanitarian Engineering: Advancing Technology for Sustainable Development,
Midterm Project: (i) Read the Liu/Passino cohesive behaviors paper (skip proofs) and simulate the N=3 (line) case for the Liu/Passino cohesive behaviors paper and study the effects of parameters; (ii) consider Section 4.5 (read it all carefully, and teach yourself the Matlab code given there) in Humanitarian Engineering: Advancing Technology for Sustainable Development, and add it into the model of group choice (the one from class based on "Modeling and Analysis of Nest-Site Selection by Honey Bee Swarms: The Speed and Accuracy Trade-off"), and study the dynamics in detail (you have to make good choices on what to study computationally using the Passino/Seeley paper, and you will have to write your own code for group selection and add in the approach in Section 4.5); (iii) Read and summarize/critique (for each critique, write a summary of the paper and then say what is good AND bad about the paper: this should take about 5 typed pages per critique): Passino K.M., Seeley T.D., Visscher P.K., "Swarm Cognition in Honey Bees," Behavioral Ecology and Sociobiology, Vol. 62, No. 3, pp. 401-414, Jan. 2008. More information to follow.
Group/Community Resource Allocation (who gets what?; 1 week): Load balancing in computer networks, The "ideal free distribution." Social foraging by honeybees.
Class:
(1) Modeling distributive justice systems and emergent fair/unfair distributions. Overview of how in social foraging a hive can achieve an ideal free distribution. Swarm cognition for allocation per social foraging by honeybees. Relations to distributive justice (and bee pollen regulation).
(2) Differential equation model, evolutionary game-theoretic, stability, and optimization perspectives: Quijano N., Passino K.M., "The Ideal Free Distribution: Theory and Engineering Application," IEEE Trans. on Systems, Man, and Cybernetics, Vol. 37, No. 1, pp. 154-165, Feb. 2007.
(3) Relations to attentional systems, and learning systems and adaptive control using multimodel methods, from Biomimicry for Optimization, Control, and Automation.
(4) Overviews of (i) Moore B.J., Finke J., Passino K.M., "Optimal Allocation of Heterogeneous Resources in Cooperative Control Scenarios," Automatica, Vol. 45, pp. 711-715, 2009, (ii) balancing modeling and analysis via paper: Burgess K.L., Passino K.M., "Stability Analysis of Load Balancing Systems", Int. Journal of Control, Vol. 61, No. 2, pp. 357-393, February 1995; and (iii) Quijano, Nicanor, Passsino, Kevin M., "Honey Bee Social Foraging Algorithms for Resource Allocation: Theory and Application," Engineering Applications of Artificial Intelligence, Vol. 23, pp. 845-861, 2010.
Homework 4: (i) Read and summarize/critique (for each critique, write a summary of the paper and then say what is good AND bad about the paper) nonlinear stochastic discrete time models, topology/low-information case, emergence, stability perspective in: Finke J. and Passino K.M., "Local Agent Requirements for Stable Emergent Group Distributions," IEEE Trans. Automatic Control, Vol. 56, No. 6, pp. 1426-1431, June 2011. Explain what is different about this paper compared to the Quijano/Passino paper above. More information to follow. Simulation of achievement of an ideal free distribution: Consider the case of N=3 habitats. Simulate either (your choice, but reccommend the Quijano paper) the ODE approach (Quijano) or distributed computing approach (Finke) and demonstrate achievement of the IFD, and how perturbations off the IFD result in returning to the IFD (stability). Study the effect of P, a measure of the size of the simplex. More information to follow.
Optional: (i) Study the Kuramoto model of coupled oscillators (synchronization), and simulate the oscillator in Section 1 of that page for your choice of parameters, with N>=10; use a Matlab movie to illustrate the dynamics. (ii) Read and critique the following papers: (i) Finke J., Quijano N., Passino K.M., "Emergence of Scale Free Networks from Ideal Free Distributions," Europhysics Letters, Vol. 82, 28004 (6 pages), April 2008; or (ii) Quijano, Nicanor, Passsino, Kevin M., "Honey Bee Social Foraging Algorithms for Resource Allocation: Theory and Application," Engineering Applications of Artificial Intelligence, Vol. 23, pp. 845-861, 2010.
Group/Community Task Processing, Scheduling, Assignment (working together: who does what, and when?; 2 weeks): Cooperative management and processing of tasks.
Class:
(1) (i) Luis Felipe Giraldo and Kevin M. Passino, "Dynamic Task Performance, Cohesion, and Communications in Human Groups," IEEE Trans. on Cybernetics, Vol. 46, No. 10, pp. 2207-2219, 2016; (ii) Pavlic T.P., Passino K.M., "Distributed and Cooperative Task Processing: Cournot Oligopolies on a Graph," IEEE Transactions on Cybernetics, Vol. 44, No. 6, pp. 774-784, June 2014.
(2) Overview of: (a) Gil A., Passino, K.M., "Stability Analysis of Network-Based Cooperative Resource Allocation Strategies," Vol. 42, pp. 245-250, Automatica, 2006, (b) Gil A., Passino K.M., Cruz J.B., "Stable Cooperative Surveillance with Information Flow Constraints," IEEE Trans. on Control Systems Technology, Vol. 16, No. 5, pp. 856-868, Sept. 2008. (c) Gil A., Passino K.M., Ganapathy S., Sparks A., "Cooperative Task Scheduling for Networked Uninhabited Air Vehicles," IEEE Trans. on Aerospace and Electronic Systems, Vol. 44, No. 2, 561-581, April 2008. (d) Moore B.J., Passino K.M., "Decentralized Redistribution for Cooperative Patrol," Int. J. Nonlinear and Robust Control, Vol. 18, pp. 165-195, Jan. 2008.
(3) Role of task assignment: Moore B.J., Passino K.M., "Distributed Task Assignment for Mobile Agents", IEEE Trans. on Automatic Control, Vol. 52, No. 4, pp. 749-753, April, 2007 (see also the supplement) and Moore B.J., Passino K.M., "Decentralized Redistribution for Cooperative Patrol," Int. J. Nonlinear and Robust Control, Vol. 18, pp. 165-195, Jan. 2008.
(4) Relations to planning/learning systems and adaptive control using multimodel methods, from Biomimicry for Optimization, Control, and Automation.
Homework 5: Read Luis Felipe Giraldo and Kevin M. Passino, "Dynamic Task Performance, Cohesion, and Communications in Human Groups," IEEE Trans. on Cybernetics, Vol. 46, No. 10, pp. 2207-2219, 2016. Simulate for the case where... more information to follow.
Group/Community Management of Resources (avoid resource over-use, fair resource use; 1 week): Cooperation for management of environmental resources, management of use of a common technology.
Class:
(1) From Section 1.6.7, 2.4.5, and 3.6.6 in Humanitarian Engineering: Advancing Technology for Sustainable Development cover the tragedy of the commons, nonlinear ODE model, feedback control strategies to manage the commons (environment) (ideas from E Ostrom), along with Section 4.12.1 on "cooperative management of community technology." Relation to the social justice idea called the "common good" in Humanitarian Engineering: Advancing Technology for Sustainable Development.
(2) Relations to MPC/planning systems. Relations to planning/learning systems and adaptive control using multimodel methods, from Biomimicry for Optimization, Control, and Automation.
Homework 6: Do homework problems 1.39, 2.38, and 3.32 in Humanitarian Engineering: Advancing Technology for Sustainable Development.
Group/Community Human Development (lending a hand; 4 weeks): Financial cooperation, community change for human development.
Class:
(1) Features of communities, (human/community) development approaches.
(2) Overview of Hugo Gonzalez Villasanti and Kevin M. Passino, “Feedback Controllers as Financial Advisors for Low-Income Individuals,” IEEE Trans. on Control Systems Technology, Vol. 25, No. 6, pp. 2194-2201, Nov. 2017.
(3) Savings clubs and cooperative community development via Hugo Gonzalez Villasanti, Felipe Giraldo, Kevin M Passino, “Feedback Control Engineering for Cooperative Community Development,” IEEE Control Systems Magazine, pp. 87-101, June 2018.
(4) Social dilemmas and group cooperation to complete a common task via Luis Felipe Giraldo and Kevin M. Passino, “Dynamics of Cooperation in a Task Completion Social Dilemma,” PLOS ONE, Vol. 12, No. 1, Jan. 26, 2017.
Homework 7: Read AF Zambrano, G Díaz, S Ramirez, LF Giraldo, H Gonzalez-Villasanti, MT Perdomo, ID Hernández, JM Godoy. Donation Networks in Underprivileged Communities. IEEE Transactions on Computational Social Systems. 2020. Simulate the networked approach, more information to be provided. Compare, via discussion, the approach to: (i) the one in Section 4.12.2 in Humanitarian Engineering: Advancing Technology for Sustainable Development; and (ii) Hugo Gonzalez Villasanti, Felipe Giraldo, Kevin M Passino, “Feedback Control Engineering for Cooperative Community Development,” IEEE Control Systems Magazine, pp. 87-101, June 2018 Explain the similarities and differences.
Group/Community Design (innovation/building/construction by diverse groups; 3 weeks): Diversity, equity, and inclusion (DEI), principles for engineering design teams. Participatory design in a community, co-design. Current research is being conducted on these topics and an overview of this work will be covered.
Class:
(1) Quantifying diversity, equity, and inclusion.
(2) Bias model.
(3) Quantifying skills.
(4) Business vs. social justice perspectives on team performance and their quantification.
(5) Optimal team formation and dynamics of functioning, along with relations between D, E, I and team performance measures.
Final Project/Exam: Research on Cooperative Systems for Social Justice
Choose one of the following problems and solve it:
You may not talk/communicate to anyone but Prof Passino to solve your chosen problem. You may ask Prof Passino questions on the problem, but this is a final exam so only points of clarification can be discussed.
Grading: Based on homeworks, midterm project, and final project (Carmen).
Relevant Books (not required):
Additional Syllabus Statements
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