The Ohio State University
Dept. Electrical and Computer Engineering
ECE 7858 Intelligent Control
Instructor: Prof. Kevin Passino 416 Dreese Laboratory, email@example.com
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.
Relevant Books (not required):
- Books treating some topics covered in class:
- Anthony N Michel and Kaining Wang, Qualitative Theory of Dynamical Systems: The Role of Stability Preserving Mappings, Marcel Dekker, NY, 1995.
- Veysel Gazi and Kevin M Passino, Swarm Stability and Optimization, Springer-Verlag, Germany, 2011.
- Dimitri Bertsekas and John N Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Prentice-Hall, 1989 (free download, republished in 1997 by Athena Scientific).
- Kevin M. Passino and Kevin L. Burgess, Stability Analysis
of Discrete Event Systems, John Wiley and Sons, NY, 1998.
- Kevin M. Passino. Humanitarian Engineering: Creating Technologies That Help People, 2nd Edition, Bede Pub., Columbus OH, 2015 (free download).
- Social groups (from the social sciences):
- Forsyth, Group Dynamics, 6th Edition, Cengage Learning, 2013. This book covers human groups, using ideas from many fields, a large range of group sizes, and group objectives. The author’s background is “social psychology.”
- Dale and Smith, Human Behavior and the Social Environment: Social Systems Theory, Allyn and Bacon, 7th Edition, 2013. This book contains a systems-theoretic view of "social work," the profession that focuses on helping individual people and groups of people.
- Kevin M. Passino, Biomimicry for Optimization, Control, and Automation, the web site of which you can go to by clicking here.
- Jeffrey T. Spooner, Manfredi Maggiore, Raul Ordonez, and Kevin M. Passino, Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques, John Wiley and Sons, NY, 2002
- Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Addison Wesley Longman, Menlo Park, CA, 1998 (free download).
- Antsaklis P.J., Passino K.M., eds., An Introduction to Intelligent and Autonomous Control, Kluwer Academic Publishers, Norwell, MA, 1993 (free download, very general hierarchical and distributed “autonomous” controllers”).
Background: The field of intelligent control has evolved significantly over the years as progress on theory, techniques, and applications has been made. Generally, the field largely started out at the individual intelligence level (typically thought to model some aspect of the intelligence of a single human or other biological system) with fuzzy control, neural networks, planning systems, attentional systems, and with a heavy focus on learning/adaptive methods for all of those. Evolutionary methods (i.e., the genetic algorithm) have been used for design of all these individual intelligent systems (and groups of such systems) and for adaptive systems. Significant work has been done on stability analysis of such intelligent controllers when used in closed-loop feedback control (especially for adaptive fuzzy/neural control). While all that work was occurring, there was an undercurrent of work on "hierarchical intelligent autonomous controllers" (very general compositions of the above intelligent systems, including distributed ones). But, as the understanding of the "biomimicry" of individual intelligence-focused methods matured, there was a shift to distributed intelligent systems and control, especially ones that were more analytically tractable than the original hierarchical intelligent control methods, with corresponding applications (e.g., autonomous robot groups). Driven by the spread of networks and parallel and distributed computing ideas, methods shifted to “multi-agent” systems, game-theoretic approaches, swarms, and biomimicry of groups of animals. This course continues along these lines, but advancing to a focus on groups of humans interacting socially.
Applications to Interacting Groups/Technologies: There are many applications of the ideas from this class to technology including: groups of autonomous / semi-autonomous robots/vehicles (land, water, air), groups of computers interacting over a wired or wireless network, distributed feedback control (e.g., with applications to temperature control, arrays of smart lights, and the smart grid), multi-agent systems (e.g., software), flexible manufacturing systems, etc. If you are interested in such applications you should view this class as a theoretical biomimicry foundation for such methods (for more details on how to transfer ideas from this class to such applications see the publications at Passino’s web site given above); however, applications to such technologies will not be considered. Yet, technology applications are a key interest, so long as they are focused on interventions to help groups, and extending the capabilities of human groups.
Course Objectives: Social systems modeling and anlaysis via gaining an understanding of modeling and qualitative analysis of networked and distributed dynamical systems, especially collective motion, agreement, choice, and allocation.
Outline (this will be updated as the Sp16 semester progresses):
- Background: Qualitative Equivalence
- Class: 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, with some additional information from Anthony N Michel and Kaining Wang, Qualitative Theory of Dynamical Systems: The Rol of Stability Preserving Mappings, Marcel Dekker, NY, 1995. Covers general dyanmical systems on a metric space, qualitative properties, stability preserving mappings, and comparison theory.
- Homework 1: Due date: Mon. Feb 8, 11am (in class). Prove that three different dynamical systems possess qualitative properties, where you choose the dynamical systems and analysis approaches (from the above paper) 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.
- Social motion and agreement: 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: (i) 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.
- Homework 2: Swarm simulation for general case (all features) 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).
- Class: (i) Cover theory from Liu/Passino paper. Uniform boundedness, uniform ultimate boundedness, identical agent case, trajectory-following case, simulations (group/individual profile tracking in presence of noise); (ii) discuss relations to textbooks on group formation, the choice of whether to join a group, and group cohesiveness; (iii) Application to distributed agreement: political elections problem formulation (candidate positions on topics, voter preferences/positions on topics, liberals/conservatives/moderates, inter-voter persuasion, etc.), model, simulation, and analysis of voter behavior and candidate choice.
- Homework 3: (i) 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. You may start by modifying your simulation for Project 1; (ii) do all proofs in the Liu/Passino noisy swarm paper, adding in all steps/explanations.
- Class: (i) Overview key concepts from other papers on swarms here (just search on the word "swarm" to see which papers); (ii) Cover model from: 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; outline of theory from Liu/Passino (cohesive), (iii) application to distributed syncrhonization in simulation as seen in the paper, (iii) discussion on impacts on modeling and analysis for the swarm motion, political elections, and other applications.
- Homework 4: (i) Simulate the N=3 (line) case for the Liu/Passino cohesive behaviors paper and study the effects of parameters; (ii) do all proofs in the Liu/Passino paper, adding in all steps/explanations, and (iii) Read and summarize: Seeley T.D., Visscher P.K., Passino K.M., "Group Decision Making in Honey Bee Swarms," American Scientist, Vol. 94, Issue 3, pp. 220-229, May/June, 2006.
- Social choice: Nest-site selection by honeybees, "swarm cognition," connections between group choice and individual/neural level choice.
- Class: Honeybee nest-site selection mathematical/computational model, noise on option assessments, low information flow, speed-accuracy trade-off, from: 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.; Swarm cognition overview from the paper: 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.
- Homework 5: Summarize and critique: (i) 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; and (ii) Computational analysis of results of giving psychological tests to groups making choices via paper: Passino, K.M., "Honey Bee Swarm Cognition: Decision-Making Performance and Adaptation," Int. J. Swarm Intelligence Research, Vol. 1, No. 2, pp. 80-97, April-June 2010.
- Social allocation: Load balancing in computer networks, distributive justice, Social foraging by honeybees. The "ideal free distribution."
- Class: Load 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. (for more details, see: Kevin M. Passino and Kevin L. Burgess, Stability Analysis
of Discrete Event Systems, John Wiley and Sons, NY, 1998). Modeling distributive justice systems and emergent distributions.
- Homework 6: Read and critique the paper, K.M. Passino and T.D. Seeley, "The Collective Intelligence of Honey Bee Colonies Produces an Ideal Free Distribution of Foragers Among Nectar Sources," unpublished manuscript, a work in progress, 2014. Read and critique survey paper on distributive justice (see Carmen). Simulation of different notions of distributive justice for a community using a "load balancing model": Suppose nodes represent people, buffers hold their money (their bank or pocket), and links represent the connectedness between people for sensing and transfers of money. Choose two concepts of distributive justice from the survey paper and simulate them for the case of N=5 people, and a topology that is a "line." Perform a comparative computational analysis of the two concepts of distributive justice, and be sure to illustrate points made in the survey paper about characteristics of the distributive approach as appropriate (e.g., how "fair" the strategy is). It would be best if you considered average behavior of the approach via Monte Carlo simulations.
- Class: Ideal free distribution: (i) 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. (ii) Nonlinear stochastic discrete time models, topology/low-information case, emergence, stability perspective: 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.
- Homework 7: Simulation of achievement of an ideal free distribution: Consider the case of N=3 habitats. Simulate either (your choice) 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). Also, read and critique one of the following two 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. (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.
Final Project: Read "Recent Developments in Role Theory" by Biddle: (i) Create a mathematical model of the elements of role theory, (ii) simulate it and show that it represents the notions of role theory, (ii) provide a mathematical proof of some property of the dynamics (e.g., convergence, stability, or boundedness). Due May 3, noon, via email to Prof Passino or by sliding it under his door at Rm 416 Dreese Labs.
Optional Subjects (depending on time/chosen focus): Biological optimization (e.g., bacteria), distributed synchronization, game theory introduction, evolutionary game theory/evolutionary dynamics/replicator dynamics (ODE model), cooperative task processing, cooperative scheduling, distributed assignment/concensus, auctions, competitive and intelligent foraging.
Grading: Homeworks and a final project. Weighting on these, for determining the final grade, will be determined at the end of class as it depends on the difficulty level of all assignments.
- Graduate standing is required by the numbering of the course per OSU policy
- ECE 5551 Discrete-time state space (state feedback, controllability, observability, Kalman filter) or an equivalent course
- ECE 6750 Linear Systems
- ECE 6754 Nonlinear Control Systems (most important for this course; if you do not know the subjects of this course, then you will need to teach it to yourself, without my help)
- ECE 5759 Optimization is useful