Angelo Loula, Ricardo Gudwin, Charbel Nino El-Hani, and Joao Queiroz

Slides:



Advertisements
Similar presentations
Approaches, Tools, and Applications Islam A. El-Shaarawy Shoubra Faculty of Eng.
Advertisements

Swarm-Based Traffic Simulation
Beyond the Centralized Mindset
Biologically Inspired Design for (more) Scalable Robots CBA Fall 2002 Cynthia Breazeal MIT Media Lab Robotic Life Group.
Slides available at: under “Material from Guest Lectures” Introduction to Semiotics Doug Brent.
[1].Edward G. Jones, Microcolumns in the Cerebral Cortex, Proc. of National Academy of Science of United States of America, vol. 97(10), 2000, pp
Un Supervised Learning & Self Organizing Maps Learning From Examples
Intelligence without Reason
Achieving Autonomicity in IoT systems via Situational-Aware, Cognitive and Social Things Orfefs Voutyras, Spyridon Gogouvitis, Achilleas Marinakis and.
Behavior-Based Artificial Intelligence Pattie Maes MIT Media-Laboratory Presentation by: Derak Berreyesa UNR, CS Department.
Towards A Multi-Agent System for Network Decision Analysis Jan Dijkstra.
Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Cognitive Robots © 2014, SNU CSE Biointelligence Lab.,
Mobile Robot Control Architectures “A Robust Layered Control System for a Mobile Robot” -- Brooks 1986 “On Three-Layer Architectures” -- Gat 1998? Presented.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Presentation on Neural Networks.. Basics Of Neural Networks Neural networks refers to a connectionist model that simulates the biophysical information.
Conducting Situated Learning in a Collaborative Virtual Environment Yongwu Miao Niels Pinkwart Ulrich Hoppe.
An Architecture for Empathic Agents. Abstract Architecture Planning + Coping Deliberated Actions Agent in the World Body Speech Facial expressions Effectors.
Towards Cognitive Robotics Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Christian.
Beyond Gazing, Pointing, and Reaching A Survey of Developmental Robotics Authors: Max Lungarella, Giorgio Metta.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Introduction to Self-Organization
Uncovering Overlap Community Structure in Complex Networks using Particle Competition Fabricio A. Liang
COGNITIVE SEMANTICS: INTRODUCTION DANA RETOVÁ CSCTR2010 – Session 1.
Roma, Meeting Mindraces 2-3 October 2006 ISTC-CNR Achievements MindRACES: From Reactive to Anticipatory Cognitive Embodied Systems (FP )
University of Windsor School of Computer Science Topics in Artificial Intelligence Fall 2008 Sept 11, 2008.
1 Research Thinking and Writing Toolbox Gordana Dodig Crnkovic School of Innovation, Design and Engineering, Mälardalen.
Learning Chapter 5.
Neural Networks and Machine Learning Applications CSC 563 Prof. Mohamed Batouche Computer Science Department CCIS – King Saud University Riyadh, Saudi.
Learning Agents MSE 2400 EaLiCaRA Spring 2015 Dr. Tom Way.
Syllabus design. Definition A syllabus is an expression of opinion on the nature of language and learning; it acts as a guide for both teacher and learner.
Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) Under the guidance of Ms. Suchilipi Nepak Presented By Prasanna.
Chapter 2. From Complex Networks to Intelligent Systems in Creating Brain-Like Intelligence, Olaf Sporns Course: Robots Learning from Humans Park, John.
AUTOMATIC CONTROL THEORY II Slovak University of Technology Faculty of Material Science and Technology in Trnava.
The highly intelligent virtual agents for modeling financial markets G. Yang 1, Y. Chen 2 and J. P. Huang 1 1 Department of Physics, Fudan University.
Artificial Intelligence and Robotics Anna Koval EC-13.
Robot Intelligence Technology Lab. Evolution of simple navigation Chapter 4 of Evolutionary Robotics Jan. 12, 2007 YongDuk Kim.
Dr. Yasser Tawfik Associate Professor of Marketing
Providing Feedback During the Learning Experience
Dr. Holly Kruse Communication Theory
Lesson Overview 29.1 Elements of Behavior.
Writing Meeting.
Chapter 11: Artificial Intelligence
Self-Motivated Behaviour of Autonomous Agents
Feedback Why is it that a higher level of system organization is more stable? Definition of a system: keyword interact If one part has an effect on the.
Matthias Scheutz Articial Intelligence and Robotics Laboratory
Chapter 11: Learning Introduction
Automatic Control (AC) at ECE
Scientific Research in Computing
Artificial Intelligence Lecture No. 5
Bioagents and Biorobots David Kadleček, Michal Petrus, Pavel Nahodil
Lesson Overview 29.1 Elements of Behavior.
Housekeeping: Candidate’s Statement
Emerging. Topic Outline Industry 4.0 Internet of Things (iot) New Role of Industrial Engineers.
Ch. 2 Fundamental Concepts in Semiotics Part One
ARTIFICIAL INTELLIGENCE.
CISC 1003 Exploring Robotics
Chapter 4 Demonstrate why communication is a key factor in advertising effectiveness Explain how brand advertising works Understand the six key effects.
Use Cases CS/SWE 421 Introduction to Software Engineering Dan Fleck
Chapter 3. Artificial Neural Networks - Introduction -
Non-Symbolic AI lecture 4
CASE − Cognitive Agents for Social Environments
Emotions cse 574 winter 2004.
Responsive Architecture
Animal Behavior.
Lesson Overview 29.1 Elements of Behavior.
CHAPTER I. of EVOLUTIONARY ROBOTICS Stefano Nolfi and Dario Floreano
Artificial Bee Colony Algorithm
Learning to Communicate
Principles of Development
Introduction to Artificial Intelligence Instructor: Dr. Eduardo Urbina
Presentation transcript:

Angelo Loula, Ricardo Gudwin, Charbel Nino El-Hani, and Joao Queiroz The Emergence of Symbol-Based Communication in a Complex System of Artificial Creatures Angelo Loula, Ricardo Gudwin, Charbel Nino El-Hani, and Joao Queiroz KIMAS 2005 WALTHAM, MA, USA Robot Intelligence Technology Lab.

Index 1. Introduction 2. C.S. Peirce’s Semiotics 3. The Meaning of Emergence 4. Simulating Communicative Creature 5. Creatures in Operation 6. Self-Organization and Emergence of Symbol-based Communication Robot Intelligence Technology Lab.

1. Introduction Simulate the emergence of self-organized symbol-based communication among artificial creatures C.S.Peirce Semiotics Ethological case study of communication among animals Creatures, assuming the role of sign users and learners, behave collectively as a complex system Self organization of communicative interactions plays a major role in the emergence of symbol-based communication Robot Intelligence Technology Lab.

2. C.S. Peirce’s Semiotics Semiosis (meaning process) Pattern of behaviors that emerges through the intra/intercooperation between agents in a communication act Involves a self-corrective process whose structure exhibits an irreducible relation between sign, object and interpretant Three fundamental kinds of signs – icons, indexes, and symbols Robot Intelligence Technology Lab.

3. The Meaning of Emergence ‘emergence’ in a technical sense in the sciences of complexity Emergent properties or processes can be defined as a certain class of higher-level properties or processes related in a certain way to the microstructure of a class of systems. Robot Intelligence Technology Lab.

4. Simulating Communicative Creature Biological motivations, inspired by ethological case studies of intra-specific communication for predator warning. Alarm calls from vervet monkeys - use vocal signs Preys: teachers (sign vocalizers) and learners (sign apprentices) Predators: (terrestrial, aerial and ground predators), Trees (climbable objects) Bushes (used to hide) Teachers emitting predefined alarms for predators Learners trying to find out without explicit feedback with which predators each alarm is associated Robot Intelligence Technology Lab.

4. Simulating Communicative Creature What would happen if there were no previous alarm calls and the creatures needed to create their own repertoire of alarms Special type of prey (self-organizers) Which is a teacher and a learner at the same time Able to create, vocalize, and learn alarms, even simultaneously Sign learning depends on sign usage, which in turn depends on sign learning Aim of the experiment Observe the self-organizing dynamics of signs Starting with no specific signs to predators, a common repertoire of symbol-based alarm calls can emerge via local communicative interaction Robot Intelligence Technology Lab.

4. Simulating Communicative Creature The control mechanism is composed of drives, motivations, and behaviors Connection between sensors and actuators Control architecture inspired by behavior based approaches Robot Intelligence Technology Lab.

4. Simulating Communicative Creature Predators - Adjustment of sensors, movement, attack Behaviors: wandering, prey chasing, and resting Drives: hunger and tiredness Preys - Climb on tree, hide under bush, and vocalize alarms Communicative acts, vocalizing, interpreting and learning alarms. Behaviors (along with associative learning): vocalizing, scanning, following Behaviors: wandering, fleeing, and resting Drives: boredom, tiredness, fear, solitude, and curiosity Robot Intelligence Technology Lab.

4. Simulating Communicative Creature Self-organizers don’t have a pre-defined repertoire of alarm-predator associations Their vocalizing repertoire depends on associative memory When a predator is seen, they use the alarm with the highest association strength to that predator, or create a new alarm if none is known Alarms are created by randomly choosing one among the possible alarms that preys can emit Hebbian learning principles When sensorial data enters the work memories, the associative memory creates, or reinforces, the association between the visual item and the hearing item When an item is dropped from the work memory, related associations can be weakened, if it was not already reinforced These positive (reinforcement) and negative (weakening) adjustment cycles in the associative memory allow preys to self-organize their repertoire, and common alarm-predator associations to emerge Robot Intelligence Technology Lab.

5. Creatures in Operation Convergence to a common repertoire of associations between alarms and predators Repertoire of symbols make the preys escape when an alarm is heard, even in the absence of visual cues Robot Intelligence Technology Lab.

5. Creatures in Operation Alarms related with terrestrial predator Robot Intelligence Technology Lab.

5. Creatures in Operation Alarms related with aerial predator Robot Intelligence Technology Lab.

5. Creatures in Operation Alarms related with ground predator Robot Intelligence Technology Lab.

5. Creatures in Operation The individual association values of the associations between alarms and ground predator – Prey1, Prey2 Robot Intelligence Technology Lab.

5. Creatures in Operation The individual association values of the associations between alarms and ground predator – Prey3, Prey4 Robot Intelligence Technology Lab.

6. Self-Organization and Emergence of Symbol-based Communication Complex system Composed of many elements with nonlinear local interactions Typically conducting to a global pattern Self-organizers with local interactions of communicative acts Vocalizing adjust sign repertoire to adapt to the vocalized alarm and the context Vocalizing is affected as it relies on the learned sign associations Internal circularity leads to the self-organization of their repertoires This circularity is characterized by positive and negative feedback loops the more a sign is used the more the creatures reinforce it the less a sign is used the less it is reinforced In this self-organizing system, a systemic process (symbol-based communication), as much as a global pattern (a common repertoire of symbols), emerges from local (communicative) interactions, without any external or central control. Robot Intelligence Technology Lab.