Title: Probabilistic State Machines to describe emotions
1 Probabilistic State Machines to describe emotions Human speaks to robot you are beautiful / Thanks for a compliment P1 Robot speaks to human (will be in italic in next s) Happy state you are blonde! / I am not an idiot P0.3 you are blonde! / Do you suggest I am an idiot Unhappy state P0.7 Ironic state 2 Facial Behaviors of Maria Do I look like younger than twenty three Maria asks Response from a human
0.7 0.3 In word spotting mode human responses are short Maria smiles Maria frowns 3 Probabilistic Grammars for performances Speak Professor Perky blinks eyes twice P0.1 Speak Professor Perky Where P0.3 Who P0.5 P0.5 P0.5 Speak in some location smiles broadly Speak In the classroom shakes head Speak Doctor Lee What P0.1 P0.1 P0.1 Speak Was singing and dancing P0.1 Speak Was drinking wine A complete behavior of a robot described as a probabilistic (quantum entangled) machine . 4 Human-controlled modes of dialog/interaction Human teaches Thanks I have a lesson initialization Hello Maria Lesson finished Robot performs Robot asks Question Stop performance Questioning finished Command finished Thisshows only human speech controls no robot responses showing its state Thanks I have a question Thanks I have a command Human commands Human asks 5 The nexts will illustrate the following robot behaviors
Robot Initiated Conversation.
Human verifying what robot learned.
Other robot behaviors
Robot Performing (text music movements).
6 Robot-Receptionist Initiated Conversation Human Robot What can I do for you Robot asks This represents operation mode 7 Robot-Receptionist Initiated Conversation Human Robot What can I do for you I would like to order a table for two Robot asks 8 Robot-Receptionist Initiated Conversation Human Robot Smoking or non-smoking Robot asks 9 Robot-Receptionist Initiated Conversation Human Robot Smoking or non-smoking I do not understand Robot asks 10 Robot-Receptionist Initiated Conversation Human Robot Do you want a table in a smoking or non-smoking section of the restaurant Non-smoking section is near the terrace. Robot asks 11 Robot-Receptionist Initiated Conversation Human Robot Do you want a table in a smoking or non-smoking section of the restaurant Non-smoking section is near the terrace. A table near the terrace please Robot asks 12 Human-Initiated Conversation Human Robot Hello Maria initialization Robot asks 13 Human-Initiated Conversation Human Robot Hello Maria What can I do for you Robot asks 14 Human-Asking Human Robot Question Question Robot asks Human asks 15 Human-Asking Human Robot Question Yes you ask a question. Human asks 16 Human-Asking Human Robot What book wrote Lee Yes you ask a question. Human asks 17 Human-Asking Human Robot What book wrote Lee I have no sure information. Human asks 18 Human-Asking Human Robot Try to guess. I have no sure information. Human asks 19 Human-Asking Human Robot Try to guess. Lee wrote book Flowers. Human asks 20 Human-Asking Human Robot This is not true. Lee wrote book Flowers. Human asks 21 Human-Teaching Human ends questioning Human Robot Questioning finished Human asks Human teaches Questioning finished Robot asks Thanks I have a lesson 22 Human-Teaching Robot enters asking mode Human Robot Questioning finished What can I do for you Human asks Human teaches Questioning finished Robot asks Thanks I have a lesson 23 Human-Teaching Human starts teaching Human Robot Thanks I have a lesson What can I do for you Human asks Human teaches Questioning finished Robot asks Thanks I have a lesson 24 Human-Teaching Human Robot Thanks I have a lesson Yes Human teaches 25 Human-Teaching Human Robot I give you question-answer pattern Yes Human teaches 26 Human-Teaching Human Robot Question pattern What book Smith wrote Yes Human teaches 27 Human-Teaching Human Robot Answer pattern Smith wrote book Automata Theory Yes Human teaches 28 Human-Teaching Human Robot Checking question What book wrote Smith Yes Human teaches 29 Human-Teaching Human Robot Checking question What book wrote Smith Smith wrote book Automata Theory Human teaches 30 Human-Teaching Human Robot I give you question-answer pattern Yes Human teaches 31 Human-Teaching Human Robot Question pattern Where is room of Lee Yes Human teaches 32 Human-Teaching Human Robot Answer pattern Lee is in room 332 Yes Human teaches 33 Human-Checking what robot learned Human Robot Lesson finished Human teaches Lesson finished Question Robot asks Human asks 34 Human-Checking what robot learned Human Robot Lesson finished What can I do for you Human teaches Lesson finished Question Robot asks Human asks 35 Human-Checking what robot learned Human Robot Question What can I do for you Human teaches Lesson finished Question Robot asks Human asks 36 Human-Asking Human Robot Question Yes you ask a question. Human teaches Lesson finished Question Robot asks Human asks 37 Human-Asking Human Robot What book wrote Lee Yes you ask a question. Human asks 38 Human-Asking Human Robot What book wrote Lee I have no sure information. Human asks 39 Human-Asking Human Robot Try to guess. I have no sure information. Human asks 40 Human-Asking Human Robot Try to guess. Lee wrote book Automata Theory Observe that robot found similarity between Smith and Lee and generalized (incorrectly) Human asks 41 How we linked Behavior Dialog and Learning
The dialog/behavior has the following components
(1) Eliza-like natural language dialogs based on pattern matching and limited parsing.
Commercial products like Memoni Dog.Com Heart Alice and Doctor all use this technology very successfully for instance Alice program won the 2001 Turing competition.
This is a conversational part of the robot brain based on pattern-matching parsing and black-board principles.
It is also a kind of operating system of the robot which supervises other subroutines.
42 Behavior Dialog and Learning
(2) Subroutines with logical data base and natural language parsing (CHAT).
This is the logical part of the brain used to find connections between places timings and all kind of logical and relational reasonings such as answering questions about Japanese geography.
(3) Use of generalization and analogy in dialog on many levels.
Random and intentional linking of spoken language sound effects and facial gestures.
Use of Constructive Induction approach to help generalization analogy reasoning and probabilistic generations in verbal and non-verbal dialog like learning when to smile or turn the head off the partner.
43 Behavior Dialog and Learning
(4) Model of the robot model of the user scenario of the situation history of the dialog all used in the conversation.
(5) Use of word spotting in speech recognition rather than single word or continuous speech recognition.
(6) Avoidance of I do not know I do not understand answers from the robot.
Our robot will have always something to say in the worst case over-generalized with not valid analogies or even nonsensical and random.
44 (No Transcript) 45 (No Transcript) 46 Generalization of the Ashenhurst-Curtis decomposition model 47 This kind of tables known from Rough Sets Decision Trees etc Data Mining 48 Decomposition is hierarchical At every step many decompositions exist 49 Constructive Induction Technical Details
U. Wong and M. Perkowski A New Approach to Robots Imitation of Behaviors by Decomposition of Multiple-Valued Relations Proc. 5th Intern. Workshop on Boolean Problems Freiberg Germany Sept. 19-20 2002 pp. 265-270.
A. Mishchenko B. Steinbach and M. Perkowski An Algorithm for Bi-Decomposition of Logic Functions Proc. DAC 2001 June 18-22 Las Vegas pp. 103-108.
A. Mishchenko B. Steinbach and M. Perkowski Bi-Decomposition of Multi-Valued Relations Proc. 10th IWLS pp. 35-40 Granlibakken CA June 12-15 2001. IEEE Computer Society and ACM SIGDA.
50 Constructive Induction
Decision Trees Ashenhurst/Curtis hierarchical decomposition and Bi-Decomposition algorithms are used in our software
These methods create our subset of MVSIS system developed under Prof. Robert Brayton at University of California at Berkeley 2.
The entire MVSIS system can be also used.
The system generates robots behaviors (C program codes) from examples given by the users.
This method is used for embedded system design but we use it specifically for robot interaction.
51 Additionals with Background 52 Ashenhurst Functional Decomposition Evaluates the data function and attempts to decompose into simpler functions. F(X) H( G(B) A ) X A B X B - bound set if A B it is disjoint decomposition if A B it is non-disjoint decomposition 53 A Standard Map of function z Explain the concept of generalized dont cares Bound Set a b \ c Columns 0 and 1 and columns 0 and 2 are compatible column compatibility 2 Free Set z 54 NEW Decomposition of Multi-Valued Relations F(X) H( G(B) A ) X A B A X Relation Relation B if A B it is disjoint decomposition if A B it is non-disjoint decomposition 55 Forming a CCG from a K-Map Columns 0 and 1 and columns 0 and 2 are compatible column compatibility index 2 Column Compatibility Graph z 56 Forming a CIG from a K-Map Columns 1 and 2 are incompatible chromatic number 2 Column Incompatibility Graph 57 Constructive Induction
A unified internal language is used to describe behaviors in which text generation and facial gestures are unified.
This language is for learned behaviors.
Expressions (programs) in this language are either created by humans or induced automatically from examples given by trainers.
58 The integrated approach to robot vision and speech based dialogs 59 Open CV image processing software from Intel 60 Hidden Markov Model Based Face Recognition 61 Hidden Markov Model Based Face Recognition 62 Braitenberg Vehicles
Two sensors two motors
Many behaviors from simple rules
Control can be a combinational mapping or automaton
Combinational logic can be binary multiple-valued fuzzy and quantum.
Automaton can be binary multiple-valued fuzzy probabilistic non-deterministic or quantum (entangled)
This is a new concept in Machine Learning and Robotics
63 AND QUANTUM BREITENBERG FACES 64 Hadamard gate In standard computers probabilistic components are expensive and have aliasing. In quantum these are the cheapest gates and they are ideal random number generators. 65 Square Root of NOT Two Square root gates composed to an inverter Deterministic behaviors can be composed from probability waves. This does not happen outside quantum world. Square root controlled gate and its matrix Square root controlled hermitian and its matrix 66 Analysis of a Quantum Circuit. Matrices in Hilbert Space 67 Kronecker Product for Quantum Circuit Analysis Parallel connection of gates requires Kronecker Product. Serial connection of gates (Previous s) requires standard matrix multiplication. 68 Analysis of a Quantum Circuit 69 Analysis of a Quantum Circuit 70 Conclusion. What did we learn
(1) the more degrees of freedom the better the animation realism.
(2) synchronization of spoken text and head (especially jaw) movements are important but difficult.
(3) gestures and speech intonation of the head should be slightly exaggerated.
71 Conclusion. What did we learn(cont)
(4) the sound should be laud to cover noises coming from motors and gears and for a better theatrical effect.
(5) noise of servos can be also reduced by appropriate animation and synchronization.
(6) best available ATR and TTS packages should be applied especially those that use word spotting.
(7) use puppet theatre experiences.
72 Conclusion. What did we learn(cont)
(8) because of a too slow learning improved parameterized learning methods will be developed but also based on constructive induction.
(9) open question funny versus beautiful.
(10) either high quality voice recognition from headset or low quality in noisy room. YOU CANNOT HAVE BOTH WITH CURRENT ATR TOOLS.
The bi-decomposer of relations and other useful software used in this project can be downloaded from http//www-cad.eecs.berkeley.edu/mvsis/.
73 Conclusion Monday May 10 Intelligent Robotics Laboratory and Industrial Robotics Laboratory. Demo 10am- 2pm Thursday May 13 Convention Center. Demo 10am- 2pm Sunday June 6 PSU Balroom Smith Center. Competition and Demo 10am- 2pm Help needed. If you can program interface PCs know about networks want to help with WWW Page build robots learn advanced theories . You are welcome to the lab.
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