Professor: Marek A. Perkowski, Electrical Engineering.

Embedded Intelligent Robotics.

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1. Review chapter 9 from Luger and especially trees induction and section 9.5.4. on Analogical Reasoning.
2. Decision Trees Introduction to Decision Trees. Useful in many homeworks and projects.
3. Information theory in learning. Trees, expressions. Advanced Material. Taught only sometimes in detail, when useful in a project. Covers various types of Decision Trees and Diagrams, concepts of information theory and links to digital logic design.
4. Reasoning types. In PPT format. Easy introduction to knowledge and reasoning. No math or software ideas.
5. Types of reasoning. In PPT format. Discussion of reasoning and decision-making methods that may be used in projects.
6. Explanation-based reasoning and SOAR. Architecture of AI. Reasoning. In PPT format.
7. Reasoning by analogy. In PPT format. Many good examples of reasoning systems.
8. Reasoning by analogy in Prolog. In PPT format. A very short intro to Prolog language, few examples of short programs, and a program to reason by analogy.
9. auxiliary1.ppt More examnples of the representation problem. State Space, Frames. Rule-based.
10. auxiliary2.ppt Making use of prior knowledge. Analogy. Computing metaphors. Constructivist approaches. Practical learning methods. Learning by examples, experimentation, chunking, adaptation. Examples.
11. auxiliary3-Analogical-learning.ppt Learning by analogy. The structure mapping theory. Determination. Problem solving by analogy.
    
12. TASK: Using Prolog language, implement a complete program for a soccer robot that uses a decision tree to evaluate the situation. Instead of two-robot soccer you can use any other game between robots, for instance fencing or sumo. In another variant of this homework, you should implement a program that uses previously stored knowledge of a robot (mobile or stationary) in order to solve new tasks. The main condition of this project is that the software must be written in Prolog. The robot may be simulated.

1. Multi-agents. Environments. Coordination. Communication and Messages. Protocols. KQML. Ontologies. Blackboard Systems. From Luger. In PDF format.
2. Paper by Lenat about agents. Very good. This paper explains philosophy of the famous CYC system based on agents interaction.
   
3. TASK: Using Prolog language, implement a complete system for question-answering about professors and departments in MCECS at PSU. There should be separate agents who are experts on various areas of information that a visitor to MCECS may ask. The natural language interface for the system should be greatly simplified. In this project you should concentrate on implementation of agents in Prolog.

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1. X011. Behavior based robots and architectures.ppt Subsumption architecture. Sense-Plan-act architecture. Emergence and Autonomy. Behavior decomposition. Using FSMs. Arbitration and Debugging.
2. X012. Affective-Computing.ppt Basic ideas about robot emotions, affects, examples, ideas and programming. Very easy. Links to previous works that you can find on Internet.
3. X013. More advanced Projects.ppt Subsumption architectures with simple sensors. Easy.
   
4. TASK: This project is related to robots that simulate some kind of emotions. You can use any humanoid or animal-like robot from Portland Cyber Theatre. The behaviors are based on vision from KINECT and natural language plus gestures.

1. Braitenberg Vehicles. Behavioral Robots. Quantum Braitenberg Vehicles. You don not to be expert in quantum computing to understand how to program these robots.
2. Simple and Quantum Braitenberg Vehicles.
3. Quantum Braitenberg and advanced behavioral architectures.
4. One more paper by Arushi on QBV is missing.!!!!
5. 7ROBODEM.AVI Our robot.
6. ApplicationsClip.mpg Teenage student Yale Fan talks about his nationally acclaimed research at PSU. You do not have to understand this text to be able to do an easy homework on Braitenberg Vehicles or even on Quantum Braitenberg Vehicles.
7. Talking-lions-2.avi Our lions who argue permanently - from Hahoe Theatre. Built by Martin Lukac and Kyle.
8. biped.MOV The Lego biped built by a teenager Michal Woyke in Poland.
9. braiten-clp1.wmv Braitenberg Vehicle of Arushi, a teenage girl who works on quantum robots. This robot is afraid of light.
10. braiten-clp2.wmv Braitenberg Vehicle of Arushi. This robot is afraid of objects, obstacles.
11. braiten-clp3.wmv Braitenberg Vehicle of Arushi. Agressive robot.
12. braiten-clp4.wmv Braitenberg Vehicle of Arushi. Light following with switched behavior.
13. braiten-clp5.wmv Braitenberg Vehicle of Arushi. Turns left from objects.
14. braiten-clp6.wmv Braitenberg Vehicle of Arushi. Likes music.
15. braiten-clp7.wmv Braitenberg Vehicle of Arushi. Attacks music source but is afraid of a hand.
16. robohead-clp1.wmv Braitenberg Vehicle of Arushi. Einsteind-Podolsky-Rosen robot called Mister Quantum Potato Head. Illustrates EPR entanglement with movement of mouth and eyes. (11 measured). Pay attention to delay when it does nothing (00 measured)
17. robohead-clp2.wmv Braitenberg Vehicle of Arushi. Einsteind-Podolsky-Rosen robot called Mister Quantum Potato Head.
18. robohead-clp3.wmv Braitenberg Vehicle of Arushi. Einsteind-Podolsky-Rosen robot called Mister Quantum Potato Head.
19. Class with robot project. A robot connected to quantum computer.
    
    
20. TASK: Using any programming language, implement an arbitrary Braitenberg Vehicle. The system should allow us to compare simple robot behaviors using: (a) standard binary (Boolean) logic, (b) fuzzy logic, (c) Quantum logic. Keep it simple, follow the examples above. In this case your task is not to build an impressive robot but to compare three types of logic that may be used in a simple behavioral model. You can reuse your natural language and/or fuzzy software from other homeworks.

1. Towards Robot Theatre and fundamental models in robotics. This slide presentation has many ideas that may be used to create robots for autistic children.
2. Androids. Movie about our "artificial idiot" robot Do you want to build a robot like this?
3. Androids. On designing robots that look like humans. Robot toys, puppets and dools. Famous ROBOTA dolls-robots.
4. Autism-and-Robots.
5. Generative Art. How to use evolutionary ideas to produce art.
6. Subsumption Theory.
7. Examples-of-Subsumption Architectures in Mobile Robots.
8. Reactive software. Examples of programs in a variant of C language for simple robots.
9. Behavioral Architectures.
10. Decision Trees. New Version. ppt. .
    
11. TASK: Using any programming language, implement an arbitrary behavioral robot. It can be subsumption architecture or a Braitenberg Vehicle, for instance. You should integrate knowledge from any previous homework such as fuzzy logic or evalutionary programming. The goal of this project is to create a certain toy for autistic children. Read about autism and how robots can help. Interactive toys for autistic children are also very similar to interactive actors in robot theatre.

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1. Embedded Systems. This lecture may be useful to select your project design idea.
2. Types of robots This lecture may be useful to help you design your robot arm.
3. Actuators. This is a basic lecture about motors and actuators for robots. It may help you in projects and homeworks such as:
   1. Robot arm design.
   2. Robot head design.
   3. Robot leg design.
   4. Robot body design.
4. Robot Examples. My requirement is: the robot should have 2 hands, head and torso and should be a mobile robot.
5. X003. Tetrix Mechanical Design.pptx
6. X003A. Tetrix Robot Mechanical Examples.pptx
7. Evolutionary Methods in Design. Art. Lego bridges of Pollack. Evolutionary art and how it relates to robotics. Mutator program. Computer Evolution of Buildable Objects of Pablo Funes and Jordan Pollack. This may be used to design a robot automatically.
   
   
8. TASK: design mechanically a part of your robot for the project. It may be any of the above and more. More slides about design will be added.

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1. Functional Decomposition Machine.
2. Physical Design for EHW.
3. Marek Perkowski et al, ``Use of Machine Learning based on Constructive Induction in Dialogs with Robotics Heads''. Proc. ICORR 2003, the International Conference on Rehabilitation Robotics, Daejeon, Korea.
4. Marek Perkowski and Stanislaw Grygiel, ``Decomposition of Relations: A New Approach to Constructive Induction in Machine Learning and Data Mining - An Overview.'' Proc. Telecommunication Conference, Poland. May 2001.
5. Slides to the above.
6. B. Steinbach, A. Mishchenko, M. Perkowski, ``An Algorithm for Bi-Decomposition of Logic Functions,'' Proceedings of DAC 2001.
7. Please read again the Chapter 15 from Luger's book if you have not done so yet. (formerly called, "Computers for logic and logic programming" in the catalog).
8. This material is covered by individual projects only.
9. Cube Calculus Machine.
10. Basic Combinational Problems. Coloring, covering, cliques, decomposition. These are basic algorithms that have been speed up in hardware accelerators.
11. Basic Combinational Problems. Coloring, covering, cliques, decomposition. II
12. Basic Combinational Problems. Coloring, covering, cliques, decomposition. III
13. Machines of Svoboda and Sasao. August Stern. Tautology and satisfiability. Simulation Machines.
14. Logic Design Machines. ICCAD 85.
15. Information about DEC-PERLE-1 board and projects using it.
16. Cube Calculus Machine.
17. Text of Qihong Chen and Figures. Latex and eps formats.
18. Perkowski et al, Learning Hardware using Multiple-Valued Logic. IEEE Micro 2002. In PDF Format. Part 1.
19. Perkowski et al, Learning Hardware using Multiple-Valued Logic. IEEE Micro 2002. In PDF Format. Part 2.
20. Cube Calculus Machine. This is a special purpose computer for solving combinatorial decision and optimization problems. Several versions exist.
21. In-chip minimization of logic functions from DAC 2003.
22. Computer to operate on BDDs.
23. Evolvable Hardware - simple robot controller using FPGAs. Fall project using VHDL.
24. Cellular Automata - simple robot controller using FPGAs. Fall project using VHDL.
25. Decomposition Machine project. This is a computer to learn in hardware using the Ashenhurst/Curtis Decomposition.
26. Satisfiability Machine. This machine solves the satisfiability formula in hardware. Various variants of this machine has been designed in previous classes. Code not here.
27. Rough Set Theory Machine This machine is a cellular processor to process rough sets from the Pawlak's Theory used in Data Mining.
28. Image Processing and Pattern Recognition computer. Processors for various image processing and pattern recognition tasks. They have been never put together to one complete system.
29. Vision for robots.
30. Robot. Very simple robot controller. Many useful remarks about using VHDL tools from Mentor.
31. MACHINE FOR THE COVERING PROBLEM.
    This is a more advanced variant of the machine from the class. Similar to project above, Petrick machine and Satisfiability Machine.
32. REMARK. This project cannot be taken as a homework. It was not assigned in several years. It is only for people who look at a project that may be a beginning of a Master thesis or a PhD thesis.
    
    TASK: Develop a hardware machine for learning in real time. The machine should be using FPGAs or memristors. It is also allowed to write a parallel software using CUDA or any GPGPU system. In case of FPGAs, you can use Verilog or VHDL.

1. Introduction to the class. What is a robot. In PPT format.
2. History. In PPT format.
3. What robots are good for. In PPT format. Learn about various types of robots that exist and can be build by us.
4. Inexpensive robots. In PPT format. Learn about various types of robots that we already built in the lab. You can reuse, extend them or modify. You can build your own similar robots.
5. Lego Robots. In PPT format. Inexpensive but complete robots that we used for fast prototyping of robots in the past. Used in high schools and in various competitions.
6. Robots from Robix kit, the fastest way to control movements. In PPT format. We have two Robix kits available for students to use.
7. LANGUAGES AND SYSTEMS FOR SIMPLE ROBOTS.
   1. X016. NQC Programming.ppt
   2. X017. NXC Programming.ppt
   3. Example of software in RobotC.
   4. X018. OUR project..ppt Some ideas about our teen project for a summer. I will add more when we will know more what we want to build.
   5. Flagbot project The practical project that we have done last year.
   6. How to Design a Robot.ppt
8. OTHER TYPES OF ROBOTS.
9. Pneumatic Robots. In PPT format. We built several such robots in the past but next we switched to standard electric servos.
   
   TASK OF THIS HOMEWORK: Using LEGO, VEX or TETRIX, or just any stuff that you can find in the lab, create a simple lesson with practical exercise to build a simple mobile robot, hexapod robot or a robot arm. This robot should take the teen not more than 4 hours to assemble.

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1. Arms of Industrial robots. Part I This material is useful in projects related to building various types of robot arms.
2. Arms of Industrial robots. Part II This material is useful in projects related to building various types of robot arms.
   
   TASK: We have several industrial robots in the lab that were not used for last few years. The project is to resurrect any of these robots to become again a source of projects and homeworks in the class. Improve the documentation, create an easy exercise based on this robot.

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1. Please read from chapter 12 in Luger's book.
2. Introduction to weak methods in Theorem Proving.
3. The General Problem Solver and Difference Tables.
4. Resolution Theorem Proving.
5. Producing the Clause Form for Resolution Refutations.
6. PROLOG and Automated Reasoning.
7. Logic Programming and Prolog.
8. Please read from chapter 12 in Luger's book.
9. The binary Resolution Proof Procedure.
10. Strategies and Simplification Techniques for Resolution.
11. Answer Extraction from Resolution Refutations.
12. Further Issues in Automated Reasoning.
13. Uniform Representation for Weak Method Solutions.
14. Alternative Inference Rules.
15. Search Strategies and their use.
16. Introduction to logic programming language Prolog
17. More Prolog
18. Representation of problems using Prolog.
19. Prolog Environment.
20. METHODS OF FORMULATING AND SOLVING PROBLEMS IN LOGIC PROGRAMMING.
21. UNIFICATION IN HARDWARE.
22. MACHINES FOR LOGIC PROGRAMMING AND ARTIFICIAL INTELLIGENCE.
23. Warren Abstract Machine.
24. Japanese Fifth Generation Computer Program.
25. Data Bases.
26. Search.
27. Natural Language Processing.
28. unification.lisp
29. Ulug and Bowen.
30. Matching instead unification.
31. Lisp Machines.
32. Prolog Machines.
33. Parallel Prolog Machines.
34. CAM from MIT.
35. CBM in ATR Japan.
36. TASKS: (1) Learn Prolog. (2) Write a simple Natural Language program for conversation with arbitrary robot from the class. The conversation should be related to some robot behavior such as search for best action sequence for a robot arm or a mobile robot.

1. Basic functions in Lisp. In PPT format.
2. Lists in Lisp and functions on them. In PPT format.
3. Destructive list operations. In PPT format.
4. Problem solving in Common Lisp. In PPT format.
5. Introduction to search in Artificial Intelligence. In PPT format.
6. Search. In PPT format.

7. LISP PROGRAMS.

8. Many examples of very useful Lisp programs for robotics.
9. Farmer_wolf_etc_rules_only.lisp
10. farmer_wolf_goat_cabbage.lisp
11. Best First Search. Program in Lisp.
12. Breadth First Search. Search_1.lisp
13. Breadth First Search. Search_2.lisp
14. Depth First Search. Program in Lisp
    

    LISP FOR LISP PROGRAMMERS. ADVANCED LISP AND PROGRAMMING EXAMPLES.

15. Read the remaining of chapter 15 from Luger about LISP, if you are not done yet.
16. EXAMPLES OF PROGRAMS IN LISP.
17. Credit Program.
18. Expert System Shell.
19. logic_shell
20. newtons_method.lisp
21. Semantic networks. Logical reasoning systems.
22. semantic_network
23. thermostat_simulation.clos
24. OOPS in LISP.
25. thermostat_simulation.oops
    

    LISP AND EVOLUTIONARY ROBOTICS.

    1. Lisp and Evolutionary Robotics.
    2. Basic Models of robot architectures. Robot theatre as a metaphore of human robot interaction. Introduction to projects.
    3. Basic functions in Lisp.
    4. Lists in Lisp.
    5. Overview of Lisp. New.
    6. Read Chapter 21 of the textbook. "Genetic Programming".
    7. Read Chapter 22 of the textbook. "Behavior-Based Systems".
    8. Read Chapter 23 of the textbook. "Evolution of Walking Gaits".
    9. Read Chapter 16 of the textbook. "Maze Exploration".
    
    
    
26. TASK FOR A HOMEWORK: Just learn LISP and demonstrate how you can use LISP to control motions or behaviors of any robot from the lab.
    
    
27. TASK FOR A PROJECT: Create a LISP system that combines search with natural language processing for any robot in the lab.

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1. Informed Search. ppt
2. Maze Solving. NXT Robot. ppt
3. Search.ppt
4. Search formulation problems. Basic strategies. ppt
5. Micro Mouse Mazes. ppt
6. Mobile Robot Systems Examples. ppt
7. Please read from Chapter 15 from Luger's book.
8. Symbolic Expressions. The syntactic Basis of LISP.
9. Control of LISP Evaluation: quote and eval.
10. Programming in LISP; creating new functions.
11. Program Control in LISP: Conditionals and Predicates.
12. Functions, Lists and Symbolic Computing.
13. Lists as recursive structures.
14. Nested lists, Structure and CAD/CDR recursion.
15. Binding variables using SET.
16. Defining local variables using LET.
17. Data Types in Common Lisp.
18. Search in LISP. A functional approach to the Farmer, Wolf, Goat and Cabbage Problem.
19. Higher order functions and abstractions.
20. Maps and filters.
21. Functional arguments and lambda expressions.
22. Search strategies in LISP: breadth-first. Depth-First. Best-First search.
23. Pattern matching in LISP.
24. Recursive Unification.
25. Please read from Chapter 15 from Luger's book.
26. Interpreters and embedded languages (not required at this time, but very useful for projects).
27. Loigc Programming in LISP (whole section is not required this quarter).
28. A simple Logic Programming Language in LISP.
29. Streams and Stream Processing.
30. A Stream-based Logic Programming Interpreter.
31. Streams and delayed Evaluation. (whole section is not required this quarter).
32. An Expert System Shell in LISP.
33. Semantic Networks and Inheritance in LISP.
34. Object oriented programming using CLOS. (whole section is not required this quarter).
35. Learning in LISP. The ID3 Algorithm. (very useful in projects).
36. Please read Chapter 3 in Luger's book.
37. Introduction.
38. Graph Theory.
39. Structures for state space search.
40. State Space Representation of Problems.
41. Strategies for State Space Search.
42. Data-Driven and Goal-driven Search.
43. Implementing graph search.
44. Depth-First and Breadth-First Search.
45. Iterative Deepening.

    TASK: Use any well known method for navigation in labirynths (mazes). You may use an artificial maze that you can find somewhere in the laboratory or use the real map of the FAB building of PSU. The robot should go from any place to any other place in the maze and avoid obstacles. Do not use Kalman Filter or other sophisticated localization/navigation/mapping method as they will be a subject of another homework next quarter. You may use search, NN, Fuzzy, GA etc. Perhaps a combination of methods will work best.

1. Please read from chapter 9 in Luger's book.
2. Introduction.
3. A framework for Symbol-Based Learning. (SBL).
4. Version Space Search.
5. Generalization Operators and the Concept Space.
6. The candidate elimination algorithm.
7. LEX: inducing Search Heuristics.
8. Evaluating Candidate Elimination.
9. The ID3 Decision Tree Induction Algorithm.
10. Top-Down Decision Tree Induction.
11. Information theoretic test selection.
12. Evaluating ID3.
13. Decision Tree Data Issues: Bagging, Boosting.
14. Inductive Bias and Learnability.
15. The theory of learnability.
16. Knowledge and Learning.
17. Meta-Dendral.
18. Explanation-Based Learning. (EBL).
19. EBL and Knowledge-Level Learning.
20. Analogical Reasoning.
21. Unsupervised Learning.
22. Discovery and Unsupervised Learning.
23. Conceptual Clustering.
24. COBWEB and Structure of Taxonomic Knowledge.
25. Reinforcement learning.
26. The components of Reinforcement learning.
27. An Example: Tic-Tac-Toe Revisited.
28. Inference Algorithms and Applications of Reinforcement Learning.
29. Introduction to Machine Learning. In PPT format. What is Machine Learning. Why Machine Learning. A General Model of Learning Agents. Aspects of Learning systems. Recent research in Machine Learning. Major paradigms of Machine Learning. The Inductive Learning Problem. Supervised Concept Learning. Inductive Learning Framework. Nearest Neighbor Classification. Learning Decision Trees. Preference Bias. Ockham's Razor. Inductive Learning and Bias. Using probabilities to create trees: Huffman code. Rule and Decision Tree learning. Neural Network Learning. Specifying a Learning Problem. Learning to play checkers. Data Mining. Planning and control applications. Research Disciplines related to learning.
30. Introduction to Machine Learning. New Version. ppt
31. Decision-trees. In PPT format. Decision tree learning. Training examples. ID3 algorithm. Greedy Search. Information Theory background. Entropy. Use of information measures in ID3. Gain. Bias. Examples. Extensions of Decision Tree algorithms. Real-Valued Data. Noise and Overfitting. Prunning Decision Trees. Incremental Learning. Evaluation Methodology. From Decision Trees to Rules. C4.5. Homeworks and projects.
32. Trees and Diagrams for Learning. In PPT format. This is an advanced material that is not covered every years. It is covered only depending on projects. Information Theoretic Approach to Minimization in Logic Expressions, Trees, Decision Diagrams and Circuits. Shannon entropy and conditional entropy. Information theoretic measures. History of approaches. Optimal Decision Trees. Arithmetic Spectrum. Extensions to Davio and Shannon expansions. New types of decision trees and diagrams. Pseudo-Kronecker. Word-Level Decision Trees for arithmetic functions. Free Word-Level Decision Trees. Galois logic. Minimization of arithmetic expressions. Linear arithmetic.
33. Introduction to Machine Learning methods. For possible use in our projects. Some of the methods presented here are below, presented in more detail and with examples. Why learning is working. Error. Computational Learning Theory. Hypothesis evaluation. Bayesian approach to learning. Bayesian Networks. Reinforcement Learning. Passive Reinforcement Learning. Active RL. Q-learning.
34. Machine Learning, Decision Trees and Entropy.
35. Decision Diagrams and Expressions. Galois and Arithmetic Logic.
36. Machine Learning, Data Mining, Robotics.
37. Introduction to Machine Learning.
38. Decision Trees and Inductive Learning.
39. Learning and Adaptation.
40. Intro to Machine Learning and Data Mining in Robotics.
41. Famous ID3 program that learns Decision Trees. Widely used in industry. This is the basic industrial method for learning. We have several codes. Easy to program. We used in the past on the talking head projects. Read below the theory. Read theory in the Luger's book.
42. plot.lsp
43. Decision Trees. old version.
44. Introduction to Machine Learning. old version.
    
45. TASK of two homeworks:
    1. Formulate the learning task of a robot to be realized in your homework. For instance to learn how to avoid obstacles.
    2. Select the learning method, such as decision tree, NN, DNF or Ashenhurst/Curtis decomposition.
    3. Find the respective learning software on Internet or ask me.
    4. Create description of your training data as a decision table in standard format of your tool. (Good format can be found on Orange tool WWW page from Slovenia).
    5. Train you system on 70% of data.
    6. Train you system on remaining 30% of your data.
    7. Compare two methods, for instance NN and Decision Tree.
    8. Write a report with tables. Draw conclusions.

1. Maze Running. Soccer. Behavioral Robots in environments. Social behaviors.
2. Mandatory Reading.
   Read all slides that you did not read yet from previous weeks.
   Read chapter 17 from the textbook. "Map Generation".
   Read chapter 18 from the textbook. "Robot Soccer".
   Review all covered material before the midterm exam next week.
   
3. There are many robot competitions such as: (1) Micromouse, (2) evolutionary car races, (3) can collecting robots, (4) robot social behaviors.
   TASK: (1) Find rules of the compatition that you want to participate or lead your team of teenagers.
   (2) Find on Internet pages about this competition from previous years.
   (3) Select or design a robot for this competition. In most competitions you have to design and program the robot from scratch.

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1. Please read chapter 13 of Luger's book. (it will be covered again later on and in projects).
2. Role of Knowledge in Language Understanding.
3. Deconstructing Language: A Symbolic Analysis.
4. Syntax.
5. Syntax and Knowledge with ATN Parsers.
6. Stochastic Tools for Language Analysis.
7. Natural Language Applications.
8. Agents. What is an agent? Definitions. Examples. Rationality and autonomy. Types of agents. Properties and characteristics of Environments. Simple Reflex Agent. Reflex Agent with Internal State. Brooks Subsumption Architecture from the point of view of agents. Goal-based agents. Utility-based agent. The Prisoner Dilemma again and respective agents.
9. Learning fundamentals. Types of learning. Role of trees. Examples. Neural Nets - learning without explanation. Example: Neuro-Control of a robotic arm. Applications of Neural Networks. Swarm intelligence. Possible uses of learning in natural language projects.
   
   
10. Rule-Based Systems, Natural Language, Expert Systems. Natural Language Interfaces to robots, examples. Block-world. Knowledge representation in rule based expert systems. What is knowledge. Components of an expert system. Human expert characteristics. Practical examples of expert systems. Natural Language use in Expert systems. Natural Language Recognition. Chinese room discussion. Recent trends in AI.
11. Homework. Eliza-like programs for our robots. Possible approaches to AI: think vs act, like humans vs well. Eliza. Parry. How to improve these models by adding learning, reasoning and expert knowledge. The Loebner Test. Explanation of homework. Invitation to creativity. The software developed in this homework will be next used on each of your robots, especially in your talking head robots.
12. Natural Language Processing. Objectives of Natural Language Processing (NLP). History. Results. Major issues. Ambiguity. Context. Meaning. NLP Systems. SHRDLU. LUNAR. TAUM-METEO. SYSTRAN.
    TASK: Create natural language interface for your robot in any language other than LISP or PROLOG. The only conditions are:
    1. Select any robot from the lab, perhaps one of theatrical robots. You have to test and demonstrate its correct working on any robot from the lab.
    2. You can use Eliza-like approach. You may take any software from internet and just adapt it to the topic of your conversation.
    3. For two homeworks. Use grammar-based approach. Define your grammar for your tasks. Incorrect sentences should be pointed out and grammatical mistakes corrected.

1. Introduction to Artificial Neural Networks. ppt
2. Neural Networks algorithms. ppt
3. Connectionist Approach.
4. Lecture on associative processors.
5. Realization of modern associative processors.
6. Abduction. Uncertainty. Probabilistic Reasoning.
   TASK: This homework is any use of Artificial Neural Networks in robotics. It can be also a project about building a hardware model of learning based on Neural Net or Associative Processing Paradigms. Model in Verilog or VHDL.

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1. Abduction. Uncertainty. Probabilistic Reasoning. In PPT format. What is abduction. Comparing abduction, deduction and induction. Characteristics of abductive reasoning. Sources of uncertainty. Probabilistic inference. Probability of events. Bayes' Theorem. Naive Bayesian Approach. Assessment of Assumptions. Limitations of Naive Bayesian System. Bayes Belief Networks (BBN). Inference with BBN. Algorithmic Approach. Joint Tree. Stochastic simulation. MAP Problems. Noisy-OR BBN. Minimum Description Length approach. Neural Network approach. Dempster-Shafer Theory. Evidence combination. Link to Fuzzy sets and fuzzy logic. Uncertainty in rule-based systems.
   TASK: This homework is any use of any Probabilistic model in robotics. but must be not a Hidden Markov Model or Bayesian Reasoning and models based on them. It can be perhaps a simple probabilistic machine linked to a fuzzy or neural system. Make a hybrid fuzzy/probabilistic model, for instance. Apply to any robot from the lab.

1. Luger. Distributed Agents. Distributed Problem Solving. Heterogeneous task sharing. Result sharing. Cooperation. Distributed Constrained Heuristic Search. Organizational Structuring. Planning. Frame Problem. STRIPS. Distributed Planning. Centralized versus Distributed Planning. Plan Merging. Distributed Hierarchical Planning. Hierarchical Behavior-Space Search. Distributed Planning and Negotiation. Distributed Plan Representation. Post Planning Coordination. Interleaving plan, coordination and execution.
2. Planning. I. Planning Problem. Planning versus Problem Solving. Major approaches to planning. General Problem Solver (GPS). Situation Calculus Planning. Basic representations for planning. Operator/action representation. Blocks world. STRIPS planning. Goals interaction. Sussman Anomaly. State-space planning. Plan-space planning. Partial-order planning. Least committement. Non-linear plans.
3. Planning. II. Rule-based Deduction Systems. Forward production systems. AND-OR graph search. Backward Production Systems. Information Retrieval System. Planning and the Frame Problem. Control Strategies in Planning. Triangle Table. Homework and exam problems.
   TASK: This homework is any use of Robot Planning. In particular it may be robot task planning or robot motion planning, not covered by homeworks above. Rule-based systems are preferable.

1. Please read from Chapter 2 of Luger book (2002 edition) on Predicate Calculus.
2. The propositional calculus.
3. Symbols and sentences.
4. The semantics of Propositional calculus.
5. Predicate Calculus.
6. The syntax of predicates and sentence.
7. A semantics for the Predicate Calculus.
8. A "Blocks World" Example of Semantic Meaning.
9. Using Inference Rules to Produce Predicate Calculus Expressions.
10. Unification.
11. Examples of Unification.
12. Wumpus world predicate calculus. NEW VERSION. ppt
13. First order logic. NEW VERSION. ppt
14. Inference in first order logic. NEW VERSION. ppt
15. Rule Based Deduction Systems and Planning. NEW VERSION. ppt
16. Latombe on motion planning. ppt
17. Please read Chapter 2 of Luger book (2002 edition) on Predicate Calculus.
18. Wumpus World and Predicate Calculus. You can read more about Wumpus world in Russell/Norvig Book. Architecture of a knowledge-based agent. Levels of a knowledge-based agent. The Wumpus world environment. Agents in Wumpus world. Representation, reasoning and logic. The connection between sentences and facts. Logic as a Knowledge-Representation Language. Ontology and Epistemology. Propositional Logic. Semantics. Interpretation. Model. Tautology. Contradiction. Entailment. Truth Tables. Venn Diagrams. Inference rules. Soundness of Resolution Principle. Normal forms of PL sentences. Horn sentences. Entailment and derivation. Weakness of PL. Hunt the Wumpus agent.
19. First Order Logic. First Order (Predicate) Logic (FOL). Syntax. Quantifiers. Scope of quantifiers. Terms and atoms. Sentences. Translating English to FOL. Semantics of FOL. Axioms. Definitions. Theorems. Higher Order Logic. Representing Change. Situation Calculus. Frame Problem.
20. Inference in First Order Logic. Inference rules for FOL. Generalized Modus Ponens. Resolution for FOL. Converting FOL sentences to clause form. Unification examples. Unification Algorithm. Resolution refutation. Refutation Resolution Procedure. Control Strategies. Examples of Automatic Theorem Proving. Horn Clauses. Logic Programming. Prolog and issues in implementing logic programming.
21. Introduction to logic, logic reasoning. Statements. Transitive syllogisms. Simple and compound statements. Translating English to propositional statements. Truth tables. Order of operations. Equivalence of metalanguage. Conditionals and their translations. Valid forms of reasoning. Quantifiers and negations.
22. Propositional Logic. A review and few new ideas. Review. Tautologies and contradictions. Propositional versus predicate calculus. Examples of using quantifiers. Logic and ambiquity. Semidecidability.
23. Example of using a propositional calculus based agent in robotics Validity. Rules of inference. Complexity of propositional inference. Wumpus world.
24. Representation and logic. Satisfiability. Soundness. Model. Interpretation. Completeness. Resolution.
25. First Order Logic Translation from English to FOPC. Interpretation. Representing change. Block world. Problems with formalization.
26. Resolution Based Automatic Theorem Proving. Resolution examples. Resolution for question-answering. Factoring. Equality. Resolution strategies. Logic Programming.
    TASK: This project require knowledge of Prolog, or any other system based on modal or temporal logic. Demostrate a system based on rules of behavior that would be able to show robot violating the rules. It is not necessary to link this system to a real robot. The whole system may use a simulated robot(s).
    Particular tasks may include:
    1. Checking the correctness and non-contradiction of the set of rules.
    2. Analysis of states reachable by the robot.
    3. Analysis of states reachable by the robot.
    4. Robot wars. Game situations.
    5. Robot morality and ethic violations.

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1. X007. Simple Sensors in ROBOTC.ppt
2. X008. IR sensors and Encoders. LCDs.ppt
3. X009. Accelerometers and Gyros.ppt
   TASK:
   1. Attach a new type of sensor to a lab robot that does not have this sensor yet.
   2. This may be a gyro, accelerometer, a compass, a sonar, a LRF, a Kinect.
   3. Find manufacturer documentation of this sensor. Find internet pages and previous projects on this sensor.
   4. Write software to integrate this sensor with the architecture of the given robot. For instance a state machine or a Kalman filter.

1. Tree Search Methods, Applications and Architectures.
2. Please read chapter 4 of Luger's book. (Do not read section 4.3).
3. Implementing the "Best-First" Search.
4. Implementing Heuristic Evaluation Functions.
5. Heuristic Search and Expert Systemes.
6. Admissibility, Monotonicity and Informedness.
7. Complexity of search algorithms.
8. Please read chapter 5 of Luger's book. (Section 5.3 is not mandatory at this time).
9. Recursion-Based Search.
10. Pattern-Directed Search.
11. Recursive search example: A variant of Knight's Tour Problem.
12. Refining the pattern search algorithm.
13. Production Systems.
14. Definition and History.
15. Examples of Production Systems.
16. Control of search in production systems.
17. Advantages of Production Systems for AI.
18. The Blackboard Architecture for Problem-Solving.
19. Introduction to Artificial Intelligence and search. ppt
20. Uninformed Search Methods. Building goal-based agents. Problem-solving agents. States. Actions. Closed world assumption. Knowledge representation issues. Single-state and multiple-state problems. The 8 puzzle. Search formulation. Examples of Problems. Basic strategies. Depth First. Breadth first. Illustration of spaces as trees. Search Complexity. Avoidance of duplication. State space versus solution (search) space. Formalization. General search algorithms. Bookkeeping. Evaluation search strategies. Uniform cost strategy. Depth-first iterative deepening. Bidirectional search. Methodological aspects. Types of problems to be represented as search. How to apply to real-world problems. Traveling Salesman, route and VLSI design problems. The importance of search outside robotics.
21. Informed search methods. Informed methods and domain-specific information. Heuristics. Best-first search. Greedy search. Beam search. Algorithm A. Algorithm A*. Iterative Deepening A*. Automatic generation of h functions. Complexity of A* search. Iterative Improvement Search. Hill Climbing. Local and Global Maxima (Minima). Simulated Annealing. Genetic Algorithm versus search: general discussion.
22. Expert systems. Knowledge-based problem solving. Expert Systems. Their characteristics. System architectures. Example of inference in expert system. Rule based reasoning. Case study: MYCIN expert system. Certainty factors in MYCIN. Rule representation. Knowledge engineering. Example Expert System Shell. Auto-Repair knowledge base. Examples in Prolog. Adding uncertainty to expert system.
23. Introduction to search. Search in Symbolic versus non-symbolic representations. Symbolic representation. Problem formulations. Reasoning, pattern matching and inferencing. Artificial Intelligence versus Natural Language. Search in NL. Differences of AI and standard computing. Types of AI computing. General characteristics of AI. Robotics as part of AI. Commercial applications of AI. Computer-Aided Instruction and Intelligent Support systems. Self-Evolving systems. Future of AI. A difficult homework related to planar geometry. (only for ambitious students).
24. Search strategies from Russell and Norvig. More examples of basic informed and uninformed strategies. Detailed analysis of Tower of Hanoi problem. Bi-directional search. Complexity of strategies.
25. Artificial Intelligence versus Robotics. Architectures. Is robotics engineering or science? Essential components of a robot. Fundamentals of robot control architectures. Reactive versus deliberative systems. Hybrid Systems. Behavior-Based Systems. Feedback Control and Cybernetics. Turtle robots of Walter Grey. Breitenberg Vehicles. History of AI and robotics. Change of paradigms. Generations of robots.
26. Advanced backtracking. In PPT format. Constraint Satisfaction Problem. Example: Map Coloring. Graph Coloring. review. Satisfiability. Applications of SAT in real world. Formal definition of a Constraint Network. Binary CSP. Crosswords. Solving constraints problems. Systematic search. Backtracking. Problems with backtracking. Consistency. Constraint propagation. K-consistency. Improving backtracking.
27. Advanced Backtracking 2. A famous example of constraints: labeling line drawings. Ordered constraint graphs. Tree-structured constraint graphs. Backtrack-free CSP. Interleaving constraint-propagation and search. Variable ordering. Value ordering. Iterative repair. Min-conflicts heuristics. Intelligent backtracking. Challenges and open problems.
    
28. TASKS: (1) Pick a robot from the lab.
    (2) Select any search problem for this robot. The search tasks may include, but are not restricted to, the following:
    (a) building a pyramide of blocks by a robot arm.
    (b) collecting the set of cans in the shortest time by a mobile robot.
    (c) Finding the shortest path in a graph for mobile or walking robot.
    (d) Avoiding obstacles in the optimal way.
    (e) Solving the Inverse Kinematics problem.
    (f) Creating all motions for a marionette robot that satisfy some constraints (like not hitting himself)
    and selecting those motions that are deemed optimal in some sense.
    (3) Write or adapt the search algorithm to your robot.
    (4) Demonstrate correct behavior of your software on the robot.

1. Introduction to Knowledge Representation. In PPT Format. Knowledge representation. Types of knowledge. Semantic Data Models. Semantic Nets. Classes. Objects. Attributes. Inheritance. Encapsulation. Polymorphism. Object Orientation. Rule Representations. Types of rules. Generalizations. Hierarchical representations. Deep Knowledge. Predicates versus frames. Facets.
2. Semantic Networks. Logical Reasoning Systems. In PPT format. Forward-chaining Production Systems. Components of Production Systems. Basic Inference Procedure. Conflict Resolution Strategies. Default Reasoning. Comparing PS and FOL. Semantic Networks. Reification. Inference by Association. ISA hierarchy. Individuals and classes. Inference by inheritance. Multiple Inheritance. Nixon Diamond. Exceptions in ISA hierarchy. From Semantic Nets to Frames. Facets. Procedural attachment. Description logic. Subsumption. KR paradigms.
3. Rule Based Deduction Systems Planning. In PPT format. Rule-Based Deduction Systems. Forward Production Systems. Backward Production Systems. Homeworks. Rule-Based approach to planning. Frame problem in planning. Examples.
4. Planning. In PPT format. The Planning Problem. Planning versus Problem Solving. Typical planning assumptions. Major approaches to planning. General Problem Solver. Situation Calculus Planning. Basic representations for planning. Operator/Action representation. Blocks world. Operators. STRIPS planning. Goal Interaction. Sussman anomaly. State-space planning. Plan-space planning. Partial-order planning. Least commitment. Non-linear planning.
5. TASKS: Write a rule based system for mobile robot with grasping arm. Use one of the robots from the lab. Demonstrate use of any concepts of semantic networks from slides above. Analyze the behavior and compare with standard search. Draw conclusions.

1. Robot soccer competitions. Learn about 2002 year class project. In PPT format.
2. Explanation of soccer project.
3. Legged Robotic Soccer Project at PSU. In the past we built a robot team of walking robots. The image processing can be partially re-used in this year projects.
   
4. TASKS: Use small hexapods. We have at least four of them in the lab. Use a camera attached above the soccer field. You can find the green soccer field in the main lab. Create a system with no other sensors to play the game of soccer. You may use 2 or 4 robots. The rules can be found on ROBOT SOCCER webpages. Demonstrate the correct operation of robots. Analyze the behaviors. Draw conclusions.

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1. Robot Examples. Programming Simple Robots in RobotC. Tetrix System. Motors and sensors.
2. X000. Tetrix_Short_Promo.wmv
3. X001. Intro - Lego NXT, Tetrix, ROBOTC and Motors.ppt
4. X002. Simple programming in ROBOTC.ppt
5. X004. Robot C complete example - robot_head_1.pdf
6. X004A. Smarter BV.ppt
7. X005. Lego NXT complete example with NXC.pptx
8. X006. Projects with simple mobile robots.ppt

1. Introduction to Talking Head and theatre projects for year 2004. Find more on these topics in Martin's www page, below, and on the link to my Korean lectures from the bottom of my main www page.
2. Introduction to our theatre in year 2005.
3. Introduction to robot theatre.
4. Our inexpensive robots. Ideas for practical realization. This will teach you inexpensive methods and items in our lab that can be used for talking head robots.
5. Emotional Robots and Immitation. This will teach you about robots' emotions and how they immitate humans. These methods can be all used in our talking heads.
6. Uland Wong and Marek Perkowski, ``A New Approach to Robot's Imitation of Behaviors by Decomposition of Multiple-Valued Relations.'' Proc. Boolean Problems, 2002. Freiberg, Germany.
   
7. TASK FOR A HOMEWORK ON ROBOT THEATRE: Select a humanoid robot. Create motions for this robot. Create a very simple action for this robot. Add text, lights and sounds. Use the "Additya Editor software" or :"Cody Hanks Editor software".
   
8. TASK FOR A PROJECT ON ROBOT THEATRE. Select two robots. Perhaps these should be of the approximately the same size. Create an action for these robots. Create their behaviors, motions, lights and sounds. Text that they speak.

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1. Design of walking robots. This is an introductory material useful if your project is a walking robot.
2. This is a simple explanation of servos and their use. Futaba and Hitec servos are used in our projects.
3. Hexapods for robot soccer.
4. Pattern Recognition Project for PSU Big Hexapod.
   
5. We have several hexapod robots in the lab. We have also two KHR-1 and two iSOBOT biped android robots in the lab. The task of this homework is to demonstrate any realistic theatrical behaviors of any of these robots.
   TASKS may include:
   1. Single robot dancing.
   2. Two robots dancing together.
   3. Two robots playing soccer or another competitive game.0
   4. Robot mimimicking playing of some instrument and talking.
   5. Any action that has been not programmed before by our students.