An Autonomous Mobile Robot Architecture Using Belief Networks and Neural Networks
|
|
- Ethan Heath
- 6 years ago
- Views:
Transcription
1 An Autonomous Mobile Robot Architecture Using Belief Networks and Neural Networks Mehran Sahami, John Lilly and Bryan Rollins Computer Science Department Stanford University Stanford, CA March 16, 1995 Abstract This paper introduces a novel mobile robot architecture based on a Situated Belief Network, a belief network that is dynamically updated as a consequence of its current environment. We initially show that it is possible to employ connectionist mechanisms to learn high-level features of the environment from the low-level (sonar) inputs of the robot. These high-level features can then be reasoned with using a belief network that is dynamically modified at run-time to produce different behaviors. We present experimental results for behaviors implemented using this architecture on an Erratic mobile robot platform in terms of both behavioral efficacy and programming efficiency. We conclude that this architecture is both effective and efficient for the control of a mobile robot. 1 Introduction While research in the field of mobile robotics has been on-going for a number of years [Fikes & Nilsson, 1971; Brooks, 1986; Saffiotti et al, 1993], there has been very little consensus reached as to what constitutes a desirable mobile robot architecture. While we by no means make an attempt to settle this debate, it is our contention that for a robot control architecture to be effective it must incorporate ways to deal with a number of issues raised by previous researchers. These issues include comprehensibility, modularity, scalability, and ease of programming. We outline these issues below and present them as features of the architecture we propose later in this paper. In order for a robot architecture to be comprehensible it must be understandable at an abstract level that divorces intention from implementation. In other words, the behaviors
2 that the architecture is to implement should be specified at some level higher than the explicit writing of code whether this be through the use of production rules, state space operators, fuzzy control rules, or assignment of probabilities to actions. Such comprehensibility is desirable since it not only allows humans to easily understand the abstract behavior of a robot, but it also provides the ability for another agent to reason about the behavior of our robot when coordination in a distributed environment is desired. Another important aspect of any computer system is that of modularity. In this respect, a system (or in our case, a mobile robot architecture) needs to allow for pieces to be easily decomposed and replaced. This allows for separate modules of the architecture to be upgraded or analyzed independently of the rest of the components of the system a crucial feature of any system that needs to be maintained over time. Scalability is a facet of robot architectures which is both essential and, unfortunately, difficult to achieve. For an architecture to be truly scalable it needs to allow for new perceptual capabilities, actuator mechanisms and behaviors to be added to the architecture without having to abandon the architecture and begin anew. While some major changes may need to be made to an existing system to realize such additional capabilities, the architecture should not have inherent limitations which make it unreasonable to apply it to a large variety of tasks and situations. Finally, an often overlooked issue in designing a robot architecture is ease of programming. If we are to design control systems that can exhibit complex behavior while at the same time being adaptable to a variety of task, it should not require us to have to generate extensive programs to do so. Moreover, we should be able to utilize as much of the existing code in the system as possible. With these goals in mind, we propose an integrated mobile robot control architecture based on belief networks and neural networks. This architecture is presented in the following section. Sections 3 and 4 discuss experimental results in implementing this 2
3 architecture on a mobile robot platform and the lessons learned from this endeavor. Finally, we present our conclusions and directions for future work. 2 Architecture The robot architecture we propose integrates several diverse elements. First, we utilize connectionist learning mechanisms to learn high-level features about the world from the low-level features of the robot (i.e. sonars). We then integrate the results of this learning with a belief network to create a Situated Belief Network [Sahami, 1995] which can be used to reason about a rapidly changing world. Finally, we add an administrative component to this architecture to plan and sequence high-level actions that should be taken by the robot to achieve its goals. 2.1 Learning Mechanisms One of the main focuses of this research stems from harnessing learning techniques to abstract the low-level sonar inputs which the robot produces into some high-level feature of the world which can be reasoned about. We refer to making this transition as the Robot Viewpoint Problem. Essentially, this problem refers to the fact that it is very difficult for humans to reason coherently about the sonar input received from the robot, and thus using this data effectively becomes a difficult reasoning and coding task for the human. Alternatively, it is much easier for humans to reason about abstract features, such as the degree to which Front-Is-Clear, when trying to decide on some action to take. Thus, we are confronted with the problem of translating the robot s viewpoint (sonars) into the human s viewpoint (abstract features). To address this problem we used neural network methods [Rumelhart et al, 1986] to cast the viewpoint translation task into a supervised machine learning task. Specifically, we attempted to learn the features Front-Is-Clear, Right-Is-Clear, and Left-Is-Clear by using a single artificial neuron for each feature. We collected data for the task by simply 3
4 having the robot collect sonar vectors from its environment and then varying the position of obstacles relative to the robot. The human supervision for the task was merely making a Boolean decision as to whether a given world configuration indicated one of the features we were trying to learn (i.e. Front-Is-Clear ). This forces the human to only make a simple yes/no decision based on their own viewpoint and not by looking at the actual sonar vector values. In our study, we collected nearly 2000 sonar vectors in real time (approximately 1 hour). Data collection was very efficient since we simply set the robot to write the sonar vectors it collected to a file while we moved obstacles around the robot ensuring, for example, that Front-Is-Clear was maintained. This file was then labeled as positive instances for the learning task. We then had the robot write another file of sonar vectors, again moving obstacles around the robot, but this time ensuring that there was always an obstacle somewhere in front of the robot to preclude Front-Is-Clear being true. This provided negative examples for our learning task. Similar methods were employed for collecting data regarding the other high-level features to be learned. We then ran the data through a gradient-decent weight learning algorithm (backpropagation applied to only one neural, also know as the delta rule) to produce a function, f, that maps seven-dimensional sonar vectors into a real value in [0, 1], indicating the degree to which some high-level feature is true. The weight updating rule is given by: W new = W old + (d - f(x, W old ))(f(x, W old ))(1 - f(x, W old ))(X) where W is the weight vector, X is the sonar vector, d {0, 1} is the desired response, and f(x,w) is the sigmoid function: f(x, W) = e -(X W) Interestingly enough, we found that the data collected for each feature were nearly linearly separable indicating that, from the robot s viewpoint, these were easily recognized features. 4
5 These results were further validated by testing the learned functions using an artificially generated clean dataset. While a complete discussion of those results is beyond the scope of this paper, we found the learned functions to be extremely accurate (on the order of 1% error). Moreover, the learned functions now provides us with a means to gauge the truth of some high-level feature given previously unseen data. We are now ready to reason about these high-level features. 2.2 Situated Belief Networks The mechanism we employ for reasoning about our high-level features is a Belief Network [Pearl, 1988] (also referred to as a Probabilistic Network, Bayes Network, or Influence Diagram in different literatures). While such methods have become popular recently in the "reasoning under uncertainty" community, they have still come under scrutiny as they rely on the subjective setting of prior probabilities to reason with. We circumvent this problem by using the learned functions for high-level features to provide us dynamic prior probabilities for some high-level feature being true. Hence, our belief network is situated in that the information which begins the reasoning process (the prior probabilities) change dynamically with the environment every time the robot gets a new set of sonar readings. Our belief network topology is shown below. Front-Is Clear Right-Is Clear Left-Is Clear Move Forward Turnright Turnleft Backup ACTUATORS Figure 1. Belief network topology. 5
6 Note that the nodes labeled X-Is-Clear each contain a function learned during the learning phase described in the previous section. The conditional probabilities in the belief network still need to be set by hand, although these conditional probability tables are small and relatively easy for a person to create. The conditional probabilities are then multiplied by the dynamic prior probabilities to produce a set of posterior probabilities for each action the robot can take. The resultant posterior probabilities are then sent to the actuators of the robot indicating the degree to which some action should be taken. Thus the conditional probability tables actually determine a behavior for the robot by providing the mechanism by which features in the world are translated into actions being taken by the robot. In order to change the behavior the robot is employing at a given point, we only need to change the values in the conditional probability tables of our network. 2.3 Administrator To allow for the behavior of the robot to change at run-time, we employ an administrator mechanism that is essentially a combination of a planner and a recognizer. The planner simply produces a very high-level sequence of actions (i.e. move-downcorridor, turnright-at-intersection, move-down-corridor, etc.) that the robot is to take to bring about a goal. The recognizer merely recognizes when one subgoal has been attained (i.e. reached the end of the corridor) and indicates that it is time to perform the next subgoal. As the recognizer moves from one subgoal to the next, it simply updates the conditional probability tables for our actions to reflect the change in behavior the robot is to undertake. The complete architecture is shown on the next page. 6
7 Front-Is Clear Right-Is Clear Left-Is Clear Move Forward Turnright Turnleft Backup Admin. ACTUATORS Figure 2. Complete control architecture topology. Mathematically, the addition of the administrator node to the belief network is equivalent to conditioning the action nodes on an additional prior probability. We can think of this prior as a selector variable that is selecting the conditional probabilities associated with a given behavior out of a larger conditional probability table. We graphically differentiate the administrator in Figure 2 to show its importance as a modular component of this architecture. This figure thus represents a complete instance of a situated belief network that is applicable to the domain of robotic control. 3 Experimental Results The architecture described above was implemented on an Erratic mobile robot. Initially, we chose to forgo use of an administrator and simply implemented an obstacle avoidance behavior by setting the right conditional probabilities to produce the intended behavior. This initial phase was just to test the viability of the architecture and also test the effectiveness of our learned function mappings from sonars to high-level features. The 7
8 obstacle avoidance behavior was achieved using the following conditional probability tables (note that F refers to Front-Is-Clear, likewise with R and L): F ~F Move F R L F R ~L F ~R L F ~R ~L ~F R L ~F R ~L ~F ~R L ~F ~R ~L Turnright F R L F R ~L F ~R L F ~R ~L ~F R L ~F R ~L ~F ~R L ~F ~R ~L Turnleft F R L F R ~L F ~R L F ~R ~L ~F R L ~F R ~L ~F ~R L ~F ~R ~L Backup Table 1. Conditional probability tables for obstacle avoidance behavior. While a cursory glance at these tables may render them incomprehensible, looking at the world states represented by the conditioning variables clearly indicates the stimuli in the world that the robot will react to given these conditional probability tables. In fact, the robot did show robust obstacle avoidance while using these conditional probabilities and the learned prior probability functions. In comparison to other methods for obstacle avoidance that we had implemented previously (fuzzy control rules [Saffiotti et al, 1993] and certainty grid-based methods [Elfes, 1990; Borenstein & Koren, 1989]), this architecture provided the most consistently robust behavior. To test the full power of this architecture, at least in a limited scope, we added an administrative module to our architecture. We hard-coded a plan into the module which was simply for the robot to move down a corridor to an intersection using the obstacle avoidance behavior, turnright at the intersection and continue to move down the corridor to the next intersection. Consequently we also added a recognizer to change the behavior of the robot when it had reached the intersection and then change it again once it had completed its turn. To make full use of the resources we had at our disposal, the recognizer employed the learned high-level features in making its decision as to when the intersection had been reached and when the robot had completed its turn. The intersection 8
9 recognition process checked to see if the robot s front and left were blocked while its right was still clear. Turn completion recognition was based simply on the robot s front becoming clear. Note that the architecture does not require the recognition module to make use of the learned features, but it does provide them for free if they could be deemed useful. Running the robot using the complete architecture showed a promising coincidence of theory and reality. In our constructed environment (cardboard boxes), the robot repeatedly was able to successfully move down the corridor, recognize when it had reached the intersection, switch behaviors to turn to the right and then continue to move down the next corridor. While we believe that some fine tunings could have made the behavior virtually flawless, the results were nevertheless very impressive. 4 Discussion If the only goal we attempted to achieve with our architecture was robust robotic control, our initial results would be quite encouraging. However, in terms of the criteria outlined in Section 1, our architecture provides many compelling advantages. The architecture presented here is comprehensible to the extent that we need not reason with low-level input from the robot, but can instead deal with high-level features thereby solving the Robot Viewpoint Problem. Moreover, the explicit topology of the belief network makes it readily apparent what we are computing, how to compute it and the values that are necessary to perform the computation. The integration of the administrator unit is also clearly motivated and its role in the overall architecture is easily understood. Perhaps an even bigger advantage of this architecture is its modularity. As explained above, the various components of the architecture (planner, recognizer, belief network connections, mapping functions for prior probabilities, etc.) can easily be modified without having the re-implement the entire controller. Moreover a variety of learning, planning or 9
10 recognizing methods can be employed to achieve the desired results as parts of the whole framework. Other methods can even be incorporated within this structure (i.e. using fuzzy rules to determine prior probabilities, certainty-grid based methods for recognition, etc.) The scalability of this architecture has not yet been thoroughly explored. However, there appears to be no reason why an extensive variety of behaviors could not achieved within this framework. Clearly this is a direction for further research. Finally, the ease of programming within this architecture was far greater than even we anticipated at the outset of this project. For example, implementing the obstacle avoidance behavior by simply specifying a set of conditional probabilities took nearly an order of magnitude less time than attempting to achieve the same behavior with fuzzy control rules. We believe this to stem primarily from the fact that we could reason about high-level features rather than working at the level of the sonars. Moreover, we could virtually forget about the raw sonar values as the learning mechanism provided an effective mapping to high-level features. We even tried an experiment to see if would could write an effective mapping function by hand. We discovered that our hand-written function performed worse than the learned function, again reinforcing the importance of learning as a way to deal with the Robot Viewpoint Problem. Nevertheless, we are not claiming that other robot control architecture do not offer some similar advantages, but only that these are important issues to consider in considering the relative worth of an agent architecture. 5 Conclusions and Future Work We have provide a new architecture for the control of a mobile robot which combines elements of machine learning and probabilistic reasoning to provide a robust control mechanism. While the initial results appear encouraging, there are still a number of issues to be explored in this work. Most notably, additional experiments need to be conducted to 10
11 measure the true scalability of this framework. This includes the implementation of additional behaviors, and consequently the use of more complex planning and goal recognition mechanisms. Moreover, as the perceptual capabilities of robots expands, new venues for learning high-level features and their integration into the belief network structure need to be explored. In the long-term we hope to use this same general architecture to address issues in domains entirely unrelated to robotics. Acknowledgments The authors are grateful for the guidance of Kurt Konolige who provided the format, encouragement and the mobile robots to conduct this research. This work has also benefitted from discussions with Nils Nilsson, Pat Langley, Jeff Shrager, and Ross Schacter. Additional thanks go to our robot, The Hoon, for providing late-night inspiration. The first author is supported by a Fred Gellert ARCS Foundation scholarship. 11
12 References [Borenstein & Koren, 1989] Borenstein, J., & Koren, Y., Real-time Obstacle Avoidance for Fast Mobile Robots. IEEE Transactions on Systems, Man and Cybernetics, Vol. 19, No. 5, pp [Brooks, 1986] Brooks, R.A., A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation, Vol. RA-2, No. 1, pp [Elfes, 1990] Elfes, A., Occupancy Grids: A Stochastic Spacial Representation of Active Robot Perception. Proceedings of the Sixth Conference on Uncertainty in AI, pp [Fikes & Nilsson, 1971] Fikes, R.E. & Nilsson, N.J., STRIPS: A new approach to the application of theorem proving to problem solving, Artificial Intelligence 2, pp [Pearl, 1988] Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann. [Rumelhart et al, 1986] Rumelhart, D.E., Hinton, G.E., & Williams, R.J., Learning Representations by Back-Propagating Errors. Nature 323, pp [Saffiotti et al, 1993] Saffiotti, A., Ruspini, E.H., & Konolige, K., A Fuzzy Controller for Flakey, An Autonomous Mobile Robot, SRI International Technical Note No. 529, March [Sahami, 1995] Sahami, M., Situated Belief Networks: An Integrated Agent Architecture Using Belief Networks and Neural Networks, working paper, Department of Computer Science, Stanford University. 12
Incorporating a Connectionist Vision Module into a Fuzzy, Behavior-Based Robot Controller
From:MAICS-97 Proceedings. Copyright 1997, AAAI (www.aaai.org). All rights reserved. Incorporating a Connectionist Vision Module into a Fuzzy, Behavior-Based Robot Controller Douglas S. Blank and J. Oliver
More informationKey-Words: - Fuzzy Behaviour Controls, Multiple Target Tracking, Obstacle Avoidance, Ultrasonic Range Finders
Fuzzy Behaviour Based Navigation of a Mobile Robot for Tracking Multiple Targets in an Unstructured Environment NASIR RAHMAN, ALI RAZA JAFRI, M. USMAN KEERIO School of Mechatronics Engineering Beijing
More informationEvolving High-Dimensional, Adaptive Camera-Based Speed Sensors
In: M.H. Hamza (ed.), Proceedings of the 21st IASTED Conference on Applied Informatics, pp. 1278-128. Held February, 1-1, 2, Insbruck, Austria Evolving High-Dimensional, Adaptive Camera-Based Speed Sensors
More informationBehaviour-Based Control. IAR Lecture 5 Barbara Webb
Behaviour-Based Control IAR Lecture 5 Barbara Webb Traditional sense-plan-act approach suggests a vertical (serial) task decomposition Sensors Actuators perception modelling planning task execution motor
More informationUnit 1: Introduction to Autonomous Robotics
Unit 1: Introduction to Autonomous Robotics Computer Science 4766/6778 Department of Computer Science Memorial University of Newfoundland January 16, 2009 COMP 4766/6778 (MUN) Course Introduction January
More informationEnhanced MLP Input-Output Mapping for Degraded Pattern Recognition
Enhanced MLP Input-Output Mapping for Degraded Pattern Recognition Shigueo Nomura and José Ricardo Gonçalves Manzan Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, MG,
More informationAIEDAM Special Issue: Sketching, and Pen-based Design Interaction Edited by: Maria C. Yang and Levent Burak Kara
AIEDAM Special Issue: Sketching, and Pen-based Design Interaction Edited by: Maria C. Yang and Levent Burak Kara Sketching has long been an essential medium of design cognition, recognized for its ability
More informationCSC384 Intro to Artificial Intelligence* *The following slides are based on Fahiem Bacchus course lecture notes.
CSC384 Intro to Artificial Intelligence* *The following slides are based on Fahiem Bacchus course lecture notes. Artificial Intelligence A branch of Computer Science. Examines how we can achieve intelligent
More informationService Robots in an Intelligent House
Service Robots in an Intelligent House Jesus Savage Bio-Robotics Laboratory biorobotics.fi-p.unam.mx School of Engineering Autonomous National University of Mexico UNAM 2017 OUTLINE Introduction A System
More informationHierarchical Controller for Robotic Soccer
Hierarchical Controller for Robotic Soccer Byron Knoll Cognitive Systems 402 April 13, 2008 ABSTRACT RoboCup is an initiative aimed at advancing Artificial Intelligence (AI) and robotics research. This
More informationMINE 432 Industrial Automation and Robotics
MINE 432 Industrial Automation and Robotics Part 3, Lecture 5 Overview of Artificial Neural Networks A. Farzanegan (Visiting Associate Professor) Fall 2014 Norman B. Keevil Institute of Mining Engineering
More informationFuzzy-Heuristic Robot Navigation in a Simulated Environment
Fuzzy-Heuristic Robot Navigation in a Simulated Environment S. K. Deshpande, M. Blumenstein and B. Verma School of Information Technology, Griffith University-Gold Coast, PMB 50, GCMC, Bundall, QLD 9726,
More informationUSING A FUZZY LOGIC CONTROL SYSTEM FOR AN XPILOT COMBAT AGENT ANDREW HUBLEY AND GARY PARKER
World Automation Congress 21 TSI Press. USING A FUZZY LOGIC CONTROL SYSTEM FOR AN XPILOT COMBAT AGENT ANDREW HUBLEY AND GARY PARKER Department of Computer Science Connecticut College New London, CT {ahubley,
More informationAPPROXIMATE KNOWLEDGE OF MANY AGENTS AND DISCOVERY SYSTEMS
Jan M. Żytkow APPROXIMATE KNOWLEDGE OF MANY AGENTS AND DISCOVERY SYSTEMS 1. Introduction Automated discovery systems have been growing rapidly throughout 1980s as a joint venture of researchers in artificial
More informationSubsumption Architecture in Swarm Robotics. Cuong Nguyen Viet 16/11/2015
Subsumption Architecture in Swarm Robotics Cuong Nguyen Viet 16/11/2015 1 Table of content Motivation Subsumption Architecture Background Architecture decomposition Implementation Swarm robotics Swarm
More informationDidier Guzzoni, Kurt Konolige, Karen Myers, Adam Cheyer, Luc Julia. SRI International 333 Ravenswood Avenue Menlo Park, CA 94025
From: AAAI Technical Report FS-98-02. Compilation copyright 1998, AAAI (www.aaai.org). All rights reserved. Robots in a Distributed Agent System Didier Guzzoni, Kurt Konolige, Karen Myers, Adam Cheyer,
More informationDipartimento di Elettronica Informazione e Bioingegneria Robotics
Dipartimento di Elettronica Informazione e Bioingegneria Robotics Behavioral robotics @ 2014 Behaviorism behave is what organisms do Behaviorism is built on this assumption, and its goal is to promote
More informationLearning Behaviors for Environment Modeling by Genetic Algorithm
Learning Behaviors for Environment Modeling by Genetic Algorithm Seiji Yamada Department of Computational Intelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokyo
More informationHUMAN-LEVEL ARTIFICIAL INTELIGENCE & COGNITIVE SCIENCE
HUMAN-LEVEL ARTIFICIAL INTELIGENCE & COGNITIVE SCIENCE Nils J. Nilsson Stanford AI Lab http://ai.stanford.edu/~nilsson Symbolic Systems 100, April 15, 2008 1 OUTLINE Computation and Intelligence Approaches
More informationNeuro-Fuzzy and Soft Computing: Fuzzy Sets. Chapter 1 of Neuro-Fuzzy and Soft Computing by Jang, Sun and Mizutani
Chapter 1 of Neuro-Fuzzy and Soft Computing by Jang, Sun and Mizutani Outline Introduction Soft Computing (SC) vs. Conventional Artificial Intelligence (AI) Neuro-Fuzzy (NF) and SC Characteristics 2 Introduction
More informationSaphira Robot Control Architecture
Saphira Robot Control Architecture Saphira Version 8.1.0 Kurt Konolige SRI International April, 2002 Copyright 2002 Kurt Konolige SRI International, Menlo Park, California 1 Saphira and Aria System Overview
More informationReinforcement Learning in Games Autonomous Learning Systems Seminar
Reinforcement Learning in Games Autonomous Learning Systems Seminar Matthias Zöllner Intelligent Autonomous Systems TU-Darmstadt zoellner@rbg.informatik.tu-darmstadt.de Betreuer: Gerhard Neumann Abstract
More informationArtificial Intelligence CS365. Amitabha Mukerjee
Artificial Intelligence CS365 Amitabha Mukerjee What is intelligence Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?" Imitation Game Acting humanly:
More informationUnit 1: Introduction to Autonomous Robotics
Unit 1: Introduction to Autonomous Robotics Computer Science 6912 Andrew Vardy Department of Computer Science Memorial University of Newfoundland May 13, 2016 COMP 6912 (MUN) Course Introduction May 13,
More informationExtracting Navigation States from a Hand-Drawn Map
Extracting Navigation States from a Hand-Drawn Map Marjorie Skubic, Pascal Matsakis, Benjamin Forrester and George Chronis Dept. of Computer Engineering and Computer Science, University of Missouri-Columbia,
More informationRobot Architectures. Prof. Holly Yanco Spring 2014
Robot Architectures Prof. Holly Yanco 91.450 Spring 2014 Three Types of Robot Architectures From Murphy 2000 Hierarchical Organization is Horizontal From Murphy 2000 Horizontal Behaviors: Accomplish Steps
More informationStanford Center for AI Safety
Stanford Center for AI Safety Clark Barrett, David L. Dill, Mykel J. Kochenderfer, Dorsa Sadigh 1 Introduction Software-based systems play important roles in many areas of modern life, including manufacturing,
More informationSMARTPHONE SENSOR BASED GESTURE RECOGNITION LIBRARY
SMARTPHONE SENSOR BASED GESTURE RECOGNITION LIBRARY Sidhesh Badrinarayan 1, Saurabh Abhale 2 1,2 Department of Information Technology, Pune Institute of Computer Technology, Pune, India ABSTRACT: Gestures
More informationThe Nature of Informatics
The Nature of Informatics Alan Bundy University of Edinburgh 19-Sep-11 1 What is Informatics? The study of the structure, behaviour, and interactions of both natural and artificial computational systems.
More informationTransactions on Information and Communications Technologies vol 1, 1993 WIT Press, ISSN
Combining multi-layer perceptrons with heuristics for reliable control chart pattern classification D.T. Pham & E. Oztemel Intelligent Systems Research Laboratory, School of Electrical, Electronic and
More informationAn Experimental Comparison of Path Planning Techniques for Teams of Mobile Robots
An Experimental Comparison of Path Planning Techniques for Teams of Mobile Robots Maren Bennewitz Wolfram Burgard Department of Computer Science, University of Freiburg, 7911 Freiburg, Germany maren,burgard
More informationTEMPORAL DIFFERENCE LEARNING IN CHINESE CHESS
TEMPORAL DIFFERENCE LEARNING IN CHINESE CHESS Thong B. Trinh, Anwer S. Bashi, Nikhil Deshpande Department of Electrical Engineering University of New Orleans New Orleans, LA 70148 Tel: (504) 280-7383 Fax:
More informationChapter 1: Introduction to Neuro-Fuzzy (NF) and Soft Computing (SC)
Chapter 1: Introduction to Neuro-Fuzzy (NF) and Soft Computing (SC) Introduction (1.1) SC Constituants and Conventional Artificial Intelligence (AI) (1.2) NF and SC Characteristics (1.3) Jyh-Shing Roger
More informationAn Integrated HMM-Based Intelligent Robotic Assembly System
An Integrated HMM-Based Intelligent Robotic Assembly System H.Y.K. Lau, K.L. Mak and M.C.C. Ngan Department of Industrial & Manufacturing Systems Engineering The University of Hong Kong, Pokfulam Road,
More informationInvestigation of Navigating Mobile Agents in Simulation Environments
Investigation of Navigating Mobile Agents in Simulation Environments Theses of the Doctoral Dissertation Richárd Szabó Department of Software Technology and Methodology Faculty of Informatics Loránd Eötvös
More informationFigure 1. Artificial Neural Network structure. B. Spiking Neural Networks Spiking Neural networks (SNNs) fall into the third generation of neural netw
Review Analysis of Pattern Recognition by Neural Network Soni Chaturvedi A.A.Khurshid Meftah Boudjelal Electronics & Comm Engg Electronics & Comm Engg Dept. of Computer Science P.I.E.T, Nagpur RCOEM, Nagpur
More informationRobots in a Distributed Agent System
Robots in a Distributed Agent System Didier Guzzoni, Kurt Konolige, Karen Myers, Adam Cheyer, Luc Julia SRI International 333 Ravenswood Avenue Menlo Park, CA 94025 guzzoni@ai.sri.com Introduction In previous
More informationRobot Architectures. Prof. Yanco , Fall 2011
Robot Architectures Prof. Holly Yanco 91.451 Fall 2011 Architectures, Slide 1 Three Types of Robot Architectures From Murphy 2000 Architectures, Slide 2 Hierarchical Organization is Horizontal From Murphy
More informationCreating an Agent of Doom: A Visual Reinforcement Learning Approach
Creating an Agent of Doom: A Visual Reinforcement Learning Approach Michael Lowney Department of Electrical Engineering Stanford University mlowney@stanford.edu Robert Mahieu Department of Electrical Engineering
More information신경망기반자동번역기술. Konkuk University Computational Intelligence Lab. 김강일
신경망기반자동번역기술 Konkuk University Computational Intelligence Lab. http://ci.konkuk.ac.kr kikim01@kunkuk.ac.kr 김강일 Index Issues in AI and Deep Learning Overview of Machine Translation Advanced Techniques in
More informationKeywords Multi-Agent, Distributed, Cooperation, Fuzzy, Multi-Robot, Communication Protocol. Fig. 1. Architecture of the Robots.
1 José Manuel Molina, Vicente Matellán, Lorenzo Sommaruga Laboratorio de Agentes Inteligentes (LAI) Departamento de Informática Avd. Butarque 15, Leganés-Madrid, SPAIN Phone: +34 1 624 94 31 Fax +34 1
More informationCOS 402 Machine Learning and Artificial Intelligence Fall Lecture 1: Intro
COS 402 Machine Learning and Artificial Intelligence Fall 2016 Lecture 1: Intro Sanjeev Arora Elad Hazan Today s Agenda Defining intelligence and AI state-of-the-art, goals Course outline AI by introspection
More informationA Numerical Approach to Understanding Oscillator Neural Networks
A Numerical Approach to Understanding Oscillator Neural Networks Natalie Klein Mentored by Jon Wilkins Networks of coupled oscillators are a form of dynamical network originally inspired by various biological
More informationWe Know Where You Are : Indoor WiFi Localization Using Neural Networks Tong Mu, Tori Fujinami, Saleil Bhat
We Know Where You Are : Indoor WiFi Localization Using Neural Networks Tong Mu, Tori Fujinami, Saleil Bhat Abstract: In this project, a neural network was trained to predict the location of a WiFi transmitter
More informationArtificial Neural Networks. Artificial Intelligence Santa Clara, 2016
Artificial Neural Networks Artificial Intelligence Santa Clara, 2016 Simulate the functioning of the brain Can simulate actual neurons: Computational neuroscience Can introduce simplified neurons: Neural
More informationArtificial Intelligence. Shobhanjana Kalita Dept. of Computer Science & Engineering Tezpur University
Artificial Intelligence Shobhanjana Kalita Dept. of Computer Science & Engineering Tezpur University What is AI? What is Intelligence? The ability to acquire and apply knowledge and skills (definition
More informationIntroduction to Machine Learning
Introduction to Machine Learning Deep Learning Barnabás Póczos Credits Many of the pictures, results, and other materials are taken from: Ruslan Salakhutdinov Joshua Bengio Geoffrey Hinton Yann LeCun 2
More informationMoving Path Planning Forward
Moving Path Planning Forward Nathan R. Sturtevant Department of Computer Science University of Denver Denver, CO, USA sturtevant@cs.du.edu Abstract. Path planning technologies have rapidly improved over
More informationClassification of Road Images for Lane Detection
Classification of Road Images for Lane Detection Mingyu Kim minkyu89@stanford.edu Insun Jang insunj@stanford.edu Eunmo Yang eyang89@stanford.edu 1. Introduction In the research on autonomous car, it is
More informationAGENT PLATFORM FOR ROBOT CONTROL IN REAL-TIME DYNAMIC ENVIRONMENTS. Nuno Sousa Eugénio Oliveira
AGENT PLATFORM FOR ROBOT CONTROL IN REAL-TIME DYNAMIC ENVIRONMENTS Nuno Sousa Eugénio Oliveira Faculdade de Egenharia da Universidade do Porto, Portugal Abstract: This paper describes a platform that enables
More informationCOMPUTATONAL INTELLIGENCE
COMPUTATONAL INTELLIGENCE October 2011 November 2011 Siegfried Nijssen partially based on slides by Uzay Kaymak Leiden Institute of Advanced Computer Science e-mail: snijssen@liacs.nl Katholieke Universiteit
More informationThe next level of intelligence: Artificial Intelligence. Innovation Day USA 2017 Princeton, March 27, 2017 Michael May, Siemens Corporate Technology
The next level of intelligence: Artificial Intelligence Innovation Day USA 2017 Princeton, March 27, 2017, Siemens Corporate Technology siemens.com/innovationusa Notes and forward-looking statements This
More informationDetecticon: A Prototype Inquiry Dialog System
Detecticon: A Prototype Inquiry Dialog System Takuya Hiraoka and Shota Motoura and Kunihiko Sadamasa Abstract A prototype inquiry dialog system, dubbed Detecticon, demonstrates its ability to handle inquiry
More informationAdvanced Robotics Introduction
Advanced Robotics Introduction Institute for Software Technology 1 Motivation Agenda Some Definitions and Thought about Autonomous Robots History Challenges Application Examples 2 http://youtu.be/rvnvnhim9kg
More informationCSE 473 Artificial Intelligence (AI) Outline
CSE 473 Artificial Intelligence (AI) Rajesh Rao (Instructor) Ravi Kiran (TA) http://www.cs.washington.edu/473 UW CSE AI faculty Goals of this course Logistics What is AI? Examples Challenges Outline 2
More informationOutline. What is AI? A brief history of AI State of the art
Introduction to AI Outline What is AI? A brief history of AI State of the art What is AI? AI is a branch of CS with connections to psychology, linguistics, economics, Goal make artificial systems solve
More informationDistributed Vision System: A Perceptual Information Infrastructure for Robot Navigation
Distributed Vision System: A Perceptual Information Infrastructure for Robot Navigation Hiroshi Ishiguro Department of Information Science, Kyoto University Sakyo-ku, Kyoto 606-01, Japan E-mail: ishiguro@kuis.kyoto-u.ac.jp
More informationES 492: SCIENCE IN THE MOVIES
UNIVERSITY OF SOUTH ALABAMA ES 492: SCIENCE IN THE MOVIES LECTURE 5: ROBOTICS AND AI PRESENTER: HANNAH BECTON TODAY'S AGENDA 1. Robotics and Real-Time Systems 2. Reacting to the environment around them
More informationThe Science In Computer Science
Editor s Introduction Ubiquity Symposium The Science In Computer Science The Computing Sciences and STEM Education by Paul S. Rosenbloom In this latest installment of The Science in Computer Science, Prof.
More informationThis list supersedes the one published in the November 2002 issue of CR.
PERIODICALS RECEIVED This is the current list of periodicals received for review in Reviews. International standard serial numbers (ISSNs) are provided to facilitate obtaining copies of articles or subscriptions.
More informationProbabilistic Navigation in Partially Observable Environments
Probabilistic Navigation in Partially Observable Environments Reid Simmons and Sven Koenig School of Computer Science, Carnegie Mellon University reids@cs.cmu.edu, skoenig@cs.cmu.edu Abstract Autonomous
More informationCapturing and Adapting Traces for Character Control in Computer Role Playing Games
Capturing and Adapting Traces for Character Control in Computer Role Playing Games Jonathan Rubin and Ashwin Ram Palo Alto Research Center 3333 Coyote Hill Road, Palo Alto, CA 94304 USA Jonathan.Rubin@parc.com,
More informationLearning to traverse doors using visual information
Mathematics and Computers in Simulation 60 (2002) 347 356 Learning to traverse doors using visual information Iñaki Monasterio, Elena Lazkano, Iñaki Rañó, Basilo Sierra Department of Computer Science and
More informationResearch Statement MAXIM LIKHACHEV
Research Statement MAXIM LIKHACHEV My long-term research goal is to develop a methodology for robust real-time decision-making in autonomous systems. To achieve this goal, my students and I research novel
More informationPlaying CHIP-8 Games with Reinforcement Learning
Playing CHIP-8 Games with Reinforcement Learning Niven Achenjang, Patrick DeMichele, Sam Rogers Stanford University Abstract We begin with some background in the history of CHIP-8 games and the use of
More informationUser Type Identification in Virtual Worlds
User Type Identification in Virtual Worlds Ruck Thawonmas, Ji-Young Ho, and Yoshitaka Matsumoto Introduction In this chapter, we discuss an approach for identification of user types in virtual worlds.
More informationRadio Deep Learning Efforts Showcase Presentation
Radio Deep Learning Efforts Showcase Presentation November 2016 hume@vt.edu www.hume.vt.edu Tim O Shea Senior Research Associate Program Overview Program Objective: Rethink fundamental approaches to how
More informationObstacle avoidance based on fuzzy logic method for mobile robots in Cluttered Environment
Obstacle avoidance based on fuzzy logic method for mobile robots in Cluttered Environment Fatma Boufera 1, Fatima Debbat 2 1,2 Mustapha Stambouli University, Math and Computer Science Department Faculty
More informationMoving Obstacle Avoidance for Mobile Robot Moving on Designated Path
Moving Obstacle Avoidance for Mobile Robot Moving on Designated Path Taichi Yamada 1, Yeow Li Sa 1 and Akihisa Ohya 1 1 Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1,
More informationResearch on Hand Gesture Recognition Using Convolutional Neural Network
Research on Hand Gesture Recognition Using Convolutional Neural Network Tian Zhaoyang a, Cheng Lee Lung b a Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China E-mail address:
More informationLearning and Using Models of Kicking Motions for Legged Robots
Learning and Using Models of Kicking Motions for Legged Robots Sonia Chernova and Manuela Veloso Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 {soniac, mmv}@cs.cmu.edu Abstract
More informationImplicit Fitness Functions for Evolving a Drawing Robot
Implicit Fitness Functions for Evolving a Drawing Robot Jon Bird, Phil Husbands, Martin Perris, Bill Bigge and Paul Brown Centre for Computational Neuroscience and Robotics University of Sussex, Brighton,
More informationTraffic Control for a Swarm of Robots: Avoiding Target Congestion
Traffic Control for a Swarm of Robots: Avoiding Target Congestion Leandro Soriano Marcolino and Luiz Chaimowicz Abstract One of the main problems in the navigation of robotic swarms is when several robots
More informationCYCLIC GENETIC ALGORITHMS FOR EVOLVING MULTI-LOOP CONTROL PROGRAMS
CYCLIC GENETIC ALGORITHMS FOR EVOLVING MULTI-LOOP CONTROL PROGRAMS GARY B. PARKER, CONNECTICUT COLLEGE, USA, parker@conncoll.edu IVO I. PARASHKEVOV, CONNECTICUT COLLEGE, USA, iipar@conncoll.edu H. JOSEPH
More informationSTRATEGO EXPERT SYSTEM SHELL
STRATEGO EXPERT SYSTEM SHELL Casper Treijtel and Leon Rothkrantz Faculty of Information Technology and Systems Delft University of Technology Mekelweg 4 2628 CD Delft University of Technology E-mail: L.J.M.Rothkrantz@cs.tudelft.nl
More informationAbstract. Most OCR systems decompose the process into several stages:
Artificial Neural Network Based On Optical Character Recognition Sameeksha Barve Computer Science Department Jawaharlal Institute of Technology, Khargone (M.P) Abstract The recognition of optical characters
More informationArtificial Intelligence
Artificial Intelligence Chapter 1 Chapter 1 1 Outline What is AI? A brief history The state of the art Chapter 1 2 What is AI? Systems that think like humans Systems that think rationally Systems that
More informationAdvanced Robotics Introduction
Advanced Robotics Introduction Institute for Software Technology 1 Agenda Motivation Some Definitions and Thought about Autonomous Robots History Challenges Application Examples 2 Bridge the Gap Mobile
More informationA Robotic Simulator Tool for Mobile Robots
2016 Published in 4th International Symposium on Innovative Technologies in Engineering and Science 3-5 November 2016 (ISITES2016 Alanya/Antalya - Turkey) A Robotic Simulator Tool for Mobile Robots 1 Mehmet
More informationCreating a 3D environment map from 2D camera images in robotics
Creating a 3D environment map from 2D camera images in robotics J.P. Niemantsverdriet jelle@niemantsverdriet.nl 4th June 2003 Timorstraat 6A 9715 LE Groningen student number: 0919462 internal advisor:
More informationCSE 473 Artificial Intelligence (AI)
CSE 473 Artificial Intelligence (AI) Rajesh Rao (Instructor) Jennifer Hanson (TA) Evan Herbst (TA) http://www.cs.washington.edu/473 Based on slides by UW CSE AI faculty, Dan Klein, Stuart Russell, Andrew
More informationSwarm Intelligence W7: Application of Machine- Learning Techniques to Automatic Control Design and Optimization
Swarm Intelligence W7: Application of Machine- Learning Techniques to Automatic Control Design and Optimization Learning to avoid obstacles Outline Problem encoding using GA and ANN Floreano and Mondada
More informationPath Planning in Dynamic Environments Using Time Warps. S. Farzan and G. N. DeSouza
Path Planning in Dynamic Environments Using Time Warps S. Farzan and G. N. DeSouza Outline Introduction Harmonic Potential Fields Rubber Band Model Time Warps Kalman Filtering Experimental Results 2 Introduction
More informationAgent and Swarm Views of Cognition in Swarm-Array Computing
Agent and Swarm Views of Cognition in Swarm-Array Computing Blesson Varghese and Gerard McKee School of Systems Engineering, University of Reading, Whiteknights Campus Reading, Berkshire, United Kingdom,
More informationMehrdad Amirghasemi a* Reza Zamani a
The roles of evolutionary computation, fitness landscape, constructive methods and local searches in the development of adaptive systems for infrastructure planning Mehrdad Amirghasemi a* Reza Zamani a
More informationComplex Mathematics Tools in Urban Studies
Complex Mathematics Tools in Urban Studies Jose Oliver, University of Alicante, Spain Taras Agryzcov, University of Alicante, Spain Leandro Tortosa, University of Alicante, Spain Jose Vicent, University
More informationIntelligent Systems. Lecture 1 - Introduction
Intelligent Systems Lecture 1 - Introduction In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is Dr.
More informationHow Explainability is Driving the Future of Artificial Intelligence. A Kyndi White Paper
How Explainability is Driving the Future of Artificial Intelligence A Kyndi White Paper 2 The term black box has long been used in science and engineering to denote technology systems and devices that
More informationArtificial Intelligence. An Introductory Course
Artificial Intelligence An Introductory Course 1 Outline 1. Introduction 2. Problems and Search 3. Knowledge Representation 4. Advanced Topics - Game Playing - Uncertainty and Imprecision - Planning -
More informationSoccer-Swarm: A Visualization Framework for the Development of Robot Soccer Players
Soccer-Swarm: A Visualization Framework for the Development of Robot Soccer Players Lorin Hochstein, Sorin Lerner, James J. Clark, and Jeremy Cooperstock Centre for Intelligent Machines Department of Computer
More informationCHAPTER 6 ANFIS BASED NEURO-FUZZY CONTROLLER
143 CHAPTER 6 ANFIS BASED NEURO-FUZZY CONTROLLER 6.1 INTRODUCTION The quality of generated electricity in power system is dependent on the system output, which has to be of constant frequency and must
More informationDesigning Toys That Come Alive: Curious Robots for Creative Play
Designing Toys That Come Alive: Curious Robots for Creative Play Kathryn Merrick School of Information Technologies and Electrical Engineering University of New South Wales, Australian Defence Force Academy
More informationGPU Computing for Cognitive Robotics
GPU Computing for Cognitive Robotics Martin Peniak, Davide Marocco, Angelo Cangelosi GPU Technology Conference, San Jose, California, 25 March, 2014 Acknowledgements This study was financed by: EU Integrating
More informationElements of Artificial Intelligence and Expert Systems
Elements of Artificial Intelligence and Expert Systems Master in Data Science for Economics, Business & Finance Nicola Basilico Dipartimento di Informatica Via Comelico 39/41-20135 Milano (MI) Ufficio
More informationAcquisition of Functional Models: Combining Adaptive Modeling and Model Composition
Acquisition of Functional Models: Combining Adaptive Modeling and Model Composition Sambasiva R. Bhatta Bell Atlantic 500 Westchester Avenue White Plains, NY 10604, USA. bhatta@basit.com Abstract Functional
More informationUsing Dynamic Capability Evaluation to Organize a Team of Cooperative, Autonomous Robots
Using Dynamic Capability Evaluation to Organize a Team of Cooperative, Autonomous Robots Eric Matson Scott DeLoach Multi-agent and Cooperative Robotics Laboratory Department of Computing and Information
More informationENHANCED HUMAN-AGENT INTERACTION: AUGMENTING INTERACTION MODELS WITH EMBODIED AGENTS BY SERAFIN BENTO. MASTER OF SCIENCE in INFORMATION SYSTEMS
BY SERAFIN BENTO MASTER OF SCIENCE in INFORMATION SYSTEMS Edmonton, Alberta September, 2015 ABSTRACT The popularity of software agents demands for more comprehensive HAI design processes. The outcome of
More informationIntro to Artificial Intelligence Lecture 1. Ahmed Sallam { }
Intro to Artificial Intelligence Lecture 1 Ahmed Sallam { http://sallam.cf } Purpose of this course Understand AI Basics Excite you about this field Definitions of AI Thinking Rationally Acting Humanly
More informationOverview Agents, environments, typical components
Overview Agents, environments, typical components CSC752 Autonomous Robotic Systems Ubbo Visser Department of Computer Science University of Miami January 23, 2017 Outline 1 Autonomous robots 2 Agents
More informationModelling and Simulation of Tactile Sensing System of Fingers for Intelligent Robotic Manipulation Control
20th International Congress on Modelling and Simulation, Adelaide, Australia, 1 6 December 2013 www.mssanz.org.au/modsim2013 Modelling and Simulation of Tactile Sensing System of Fingers for Intelligent
More information