Crowdsourcing and Simulation with Mobile Agents and the JavaScript Agent Machine

Table of Content
Introduction: Outline and Synopsis
	1.1 Outline and Introduction
	1.2 Data Processing in Sensor Networks with Multi-Agent Systems
		1.2.1 Distributed Micro-scale Data Processing in Materials
		1.2.2 Multi-Agent Systems
		1.2.3 Heterogeneous Environments
	1.3 The Agent Behaviour Model
		1.3.1 Dynamic Activity-Transition Graphs
		1.3.2 The Agent Interaction with a Tuple Space
	1.4 Agent Programming Languages and AAPL
	1.5 Agent Processing Platforms
		1.5.1 The Non-Programmable Application-specific Agent Processing Platform PCSP
		1.5.2 The Programmable Agent Processing Platform PAVM
		1.5.3 JAVM: The JavaScript PAVM
		1.5.4 Comparison of the Agent Processing Platforms
		1.5.5 JAM: The JavaScript Agent Machine
	1.6 AAPL MAS and Mobile Processes: The P-Calculus
	1.7 High-Level Synthesis of Agents and Agent Platforms
	1.8 High-level Synthesis of SoC Designs
	1.9 Simulation Techniques and Framework
		1.9.1 Behavioural Simulation
		1.9.2 Platform Simulation
		1.9.3 Simulation of Real-world Sensor Networks
		1.9.4 RTL Simulation
	1.10 Event-based Sensor Data Processing and Distribution with MAS
	1.11 Self-organizing Systems and MAS
	1.12 From Embedded Sensing to the Internet-of-Things and Sensor Clouds
	1.13 Use-Case: Structural Monitoring with MAS
		1.13.1 Machine Learning and MAS
		1.13.2 Hybrid approach of MAS and Inverse Numeric Methods
	1.14 Use-Case: Smart Energy Management with MAS and AI
	1.15 Novelty and Summary
	1.16 Structure of the Book
Agent Behaviour and Programming Model
	2.1 The Agent Computation and Interaction Model
	2.2 Activity-Transition Graphs
	2.3 Dynamic Activity-Transition Graphs (DATG)
	2.4 Agent Classes
	2.5 Communication and Interaction of Agents
	2.6 Multi-Agent Systems and Networked Processing
	2.7 The Big Thing: Domains, Networks, and Mobile Agent Processing
		2.7.1 AAPL Agents in the Bigraph Model
		2.7.2 Heterogeneous Sensor Networks in the Bigraph Model
	2.8 Distributed Process Calculus
	2.9 AAPL Programming Model and Language
		2.9.1 Overview and Summary
		2.9.2 Signal Classes
		2.9.3 Distributed Tuple-Spaces
		2.9.4 AAPL Agent Classes and Agent Instantiation
		2.9.5 Agent Identification in AAPL
		2.9.6 AAPL Activities, Transitions, Composition, and Subclasses
		2.9.7 AAPL Data Types
		2.9.8 AAPL Computational Statements
		2.9.9 AAPL Control Statements
		2.9.10 AAPL Communication
		2.9.11 AAPL Migration
		2.9.12 AAPL Reconfiguration
		2.9.13 AAPL Exception Handling
	2.10 AAPL Agents, Platforms, Bigraphs, and Mobile Processes
		2.10.1 Networks of Agent Platforms
		2.10.2 AAPL MAS and the P-Calculus
	2.11 AAPL Agents and Societies
	2.12 AAPL Agents and the BDI Architecture
		2.12.1 The BDI Architecture
		2.12.2 The AAPL-BDI Relationship
	2.13 Further Reading
Agent Communication
	3.1 Shared Memory
	3.2 Tuple Space Communication
		3.2.1 The Data Model
		3.2.2 The Operational Semantics
		3.2.3 The Synchronization Model
		3.2.4 Distributed Tuple Spaces
		3.2.5 Distribution by Mobile Agents
		3.2.6 Markings and Garbage Collection
	3.3 Communication Signals
	3.4 Comparison: Signals and Tuples
	3.5 Process Communication Calculus
		3.5.1 The P-Calculus and Tuple-Spaces
		3.5.2 The P-Calculus and Signals
	3.6 FIPA ACL
	3.7 AAPL Agents and Capability-based Remote Procedure Calls
	3.8 Further Reading
Distributed Sensor Networks
	4.1 Domains and Networks
	4.2 The Sensor Node
	4.3 The Sensor Network
		4.3.1 Communication and Network Topologies
		4.3.2 Message-passing and Routing
		4.3.3 Advanced D-Routing with Backtracking
		4.3.4 From Passive Messages to Active Agents
	4.4 Further Reading
Concurrent Communicating Sequential Processes
	5.1 Parallel Data Processing
	5.2 The original CSP Model
	5.3 Inter-Process Communication and Synchronization
	5.4 The extended CCSP Model
		5.4.1 Atomic Registers
		5.4.2 Atomic Statements
		5.4.3 Mutex
		5.4.4 Semaphore
		5.4.5 Event
		5.4.6 Barrier
		5.4.7 Timer
		5.4.8 Monitor
	5.5 Signal Flow Diagrams, CSP, and Petri-Nets
	5.6 CSP Programming Languages
	5.7 The mRTL Programming Language
	5.8 The ConPro Programming Language
		5.8.1 Process Composition
		5.8.2 Data Storage Objects
		5.8.3 Signals and Components
		5.8.4 Arrays and Structure Types
		5.8.5 Exceptions and Handling
		5.8.6 Functions and Procedures
		5.8.7 Modules
		5.8.8 The ConPro Building Blocks
		5.8.9 Control and Data Processing Statements
	5.9 Hardware Architecture
		5.9.1 Processes
		5.9.2 Modules
		5.9.3 Mutex Scheduler
		5.9.4 Functions
	5.10 Software Architecture
	5.11 Further Reading
PCSP: The Reconfigurable Application-specific Agent Platform
	6.1 Pipelined Processes
	6.2 Agent Platform Architecture
		6.2.1 Token-based Agent Processing and Petri-Nets
		6.2.2 The (R)PCSP Agent Platform
		6.2.3 Replication and Factoring
		6.2.4 Software Platform
		6.2.5 Simulation Platform
	6.3 Agent Platform and Hardware Synthesis
	6.4 Platform Simulation
	6.5 Heterogeneous Networks
	6.6 Further Reading
PAVM: The Programmable Agent Platform
	7.1 Stack Machines versa Register Machines
	7.2 Architecture: The PAVM Agent Processing Platform
		7.2.1 PAVM Overview
		7.2.2 Platform Architecture
		7.2.3 Token-based Agent Processing
		7.2.4 Instruction Format and Coding
		7.2.5 Process Scheduling and VM Assignment
	7.3 Agent FORTH: The Intermediate and the Machine Language
		7.3.1 Program Code Frame
		7.3.2 Agent Processing
		7.3.3 Agent Creation and Destruction
		7.3.4 Agent Modification and Code Morphing
		7.3.5 Tuple Database Space
		7.3.6 Signal Processing
		7.3.7 Agent Mobility
		7.3.8 Examples
	7.4 Synthesis and Transformation Rules
		7.4.1 Agent Creation using Code Morphing
		7.4.2 Agent Migration using Code Morphing
		7.4.3 Code Frame Synthesis
	7.5 The Boot Sections and Agent Processing
	7.6 Agent Platform Simulation
	7.7 Case Study: A Self-organizing System
		7.1. The Algorithms
	7.8 The JavaScript WEB Platform JAVM
		7.8.1 Capability based RPC
		7.8.2 AFS: Atomic File System Service
		7.8.3 DNS: Directory and Naming Service
		7.8.4 Broker Service
		7.8.5 Domains as Organizational Structures and the Directory Name Service
		7.8.6 The Modular Platform Architecture
	7.9 Further Reading
JAM: The JavaScript Agent Machine
	8.1 JAM: The JavaScript Agent Machine
	8.2 AgentJS: The Agent JavaScript Programming Language
		8.2.1 AgentJS: The JavaScript Object and extended Code-to-Text JSON+ Representation
		8.2.2 The AgentJS Sandbox Environment
		8.2.3 AgentJS-AAPL Relationship
	8.3 AIOS: The Agent Execution and IO Environment
		8.3.1 Agent Scheduling and Check-pointing
		8.3.2 Agent Roles
		8.3.3 The Execution Platform and Networking
		8.3.4 Agent Process Mobility and Migration
		8.3.5 Security by Capability-based Authorization and a lightweight Distributed Organization System Layer
	8.4 JAM Implementations
		8.4.1 JAMLIB
		8.4.2 JAMSH: JAM Shell
		8.4.3 JAMAPP: JAM Application Program
		8.4.4 JS Execution Platforms
		8.4.5 JAM Connectivity
	8.5 Performance Evaluation
		8.5.1 Watchdog Control and Time Slicing
	8.6 SEJAM: The JavaScript Agent Simulator
	8.7 Heterogeneous Environments
	8.8 Further Reading
Self-Organizing Multi-Agent Systems
	9.1 Introduction to Self-Organizing Systems
	9.2 Self-organizing Distributed Feature Recognition
		9.2.1 Explorer Agent Behaviour Model
		9.2.2 Some Simulation Experiments of a Sensor Network
	9.3 Self-organizing Event-based Sensor Data Processing and Distribution
		9.3.1 Event Agent Behaviour
		9.3.2 Simulation Experiments of a Sensor Network
		9.3.3 Interaction of Event and Explorer Agents
	9.4 Self-organizing Energy Management and Distribution
		9.4.1 The Mobile Smart Energy Management Agent
		9.4.2 The Immobile Sensor Node Energy Management Agent
	9.5 Further Reading
ML: Machine Learning and Agents
	10.1 Introduction to Machine Learning
	10.2 Decision Trees
	10.3 Artificial Neuronal Networks
	10.4 Learning with Agents
	10.5 Distributed Learning
		10.5.1 Event-based Sensor Processing
		10.5.2 Distributed Learning Algorithm DINN
		10.5.3 Distributed Learning with MAS
		10.5.4 Distributed Learning: Case Study
	10.6 Incremental Learning
		10.6.1 Incremental Learning Algorithm I2NN
		10.6.2 Distributed Incremental Learning Algorithm DI2NN
		10.6.3 Distributed Incremental Learning MAS
		10.6.4 Distributed Incremental Learning: Case Study
	10.7 Further Reading
Simulation
	11.1 The SeSAm Agent Simulator
	11.2 Behavioural AAPL MAS Simulation
	11.3 Simulation of Real-world Sensor Networks
	11.4 PCSP Platform Simulation
		11.4.1 The Simulation Model
		11.4.2 Performance Analysis with an Use-case
	11.5 The SEM Simulation Programming Language
		11.5.1 SEM Classes Model
		11.5.2 SEM Definitions, Expressions, Values, and Types
		11.5.3 SEM Paths
		11.5.4 SEM Lists, Iterators, Arrays
		11.5.5 SEM Sequential Composition, Branches, and Loops
		11.5.6 SEM Shapes
		11.5.7 SEM Example
	11.6 SEJAM: Simulation Environment for JAM
	11.7 Multi-Domain Simulation with SEJAM2P
	11.8 Further Reading
Synthesis
	12.1 The Big Picture: All together
	12.2 SynDK: The Synthesis Development Toolkit
		12.2.1 The Graph Database centric Synthesis Approach
		12.2.2 Virtual Database Organization
		12.2.3 The VDB Software Architecture
		12.2.4 Structure Type Definition Schemas
		12.2.5 Tagged Values
		12.2.6 Programming of Compilers and LaCo: The Language Compiler
		12.2.7 Abstract Syntax and VDB Graphs
		12.2.8 VPL: VDB Programming Interface and Interpreter
		12.2.9 Compiling a Compiler
		12.2.10 Conditional and Parametric Compiling
	12.3 Agent and Agent Platform Synthesis
		12.3.1 Non-programmable Agent Platform Synthesis
		12.3.2 Programmable Agent Platform Synthesis
	12.4 The Agent Simulation Compiler SEMC
	12.5 ConPro SoC High-level Synthesis
		12.5.1 Synthesis Flow
		12.5.2 Microcode Intermediate Representation
		12.5.3 Synthesis Rules
		12.5.4 Reference Stack (RS) Optimizer and Scheduler
		12.5.5 Basic Block Scheduling and Data Path Parallelization
		12.5.6 RTL Synthesis and VHDL Model
		12.5.7 Dining Philosopher Example
		12.5.8 Complex ConPro SoC Design Case Study
	12.6 Further Reading
Energy Management
	13.1 Power Analysis and Algorithmic Selection
		13.1.1 Low-power Agent Processing
	13.2 Smart Energy Management with Artificial Intelligence
		13.2.1 Introduction
		13.2.2 Communication, Data and Energy Transport
		13.2.3 Multi-Agent Interaction Model and Implementation
		13.2.4 Analysis and Experimental Results of the SEM
		13.2.5 Algorithmic Selection and SEM
	13.3 Further Reading
Use-Cases Environmental Perception, Load Monitoring, and Manufacturing
	14.1 Sensorial Material I: A Flat Perceptive Sheet and Machine Learning
		14.1.1 Machine Learning and Multi-Agent Systems
		14.1.2 Experimental Results using Machine Based Learning with Metal Plate
		14.1.3 Experimental Results using Machine Based Learning with a Rubber Plate
	14.2 Sensorial Material II: A Flat Perceptive Plate and Inverse Numeric
	14.3 Sensorial Material III: A Perceptive Modular Robot Arm
	14.4 Sensorial Material IV: A Perceptive Robotic Gripper
	14.5 Sensor Clouds: Adaptive Cloud-based Design and Manufacturing
	14.6 Sensor Networks: Distributed Earthquake Monitoring
	14.7 Crowd Sensing
	14.8 Further Reading
Material-Integrated Sensing Systems
	15.1 The Sensorial Material
	15.2 Integration Levels
		15.2.1 Intrinsic Sensing Materials
		15.2.2 Volume Integration of Sensing
		15.2.3 Surface Integration of Sensing
		15.2.4 Surface Application of Sensing
	15.3 Integration Technologies and Sensorial Materials
		15.3.1 Monolithic Integration
	15.4 Digital Logic Technologies
		15.4.1 Register-Transfer Architecture
		15.4.2 Semi- and Full-custom CMOS/Si-based ASIC Technologies
		15.4.3 Rapid Prototyping with FPGA Technologies
		15.4.4 Printed and Organic Digital Circuits
	15.5 Computational Constraints
	15.6 Further Reading
Abbreviations, Notations, Symbols
	A.1 Abbreviations
	A.2 Symbols
	A.3 Notation
		A.3.1 AAPL Short Notation
		A.3.2 Rules
Publications and References
	R.1 Publications
	R.2 Lectures
	R.3 Supervised Thesises
	R.4 Bibliography
Index