This document provides an introduction to autonomous mobile agents. It outlines the basics of software agents and defines autonomous mobile agents as components that can operate without direct human intervention, control their own actions based on their internal state and knowledge, and migrate between machines. The document discusses advantages of mobile agents and outlines some example agent technologies like Aglets, JumpingBeans, Voyager, and D'Agents, focusing on their features and architectures. It also addresses factors that impact agent performance and security considerations for mobile agents.
Distributed Artificial Intelligence with Multi-Agent Systems for MECTeemu Leppänen
In this paper, the Multi-access Edge Computing (MEC) system architecture, as defined by the ETSI standards, is modeled as a multi-agent system. MEC system management services and application execution components are designed as software agents, facilitating distributed artificial intelligence capabilities in their operation and cooperation. Further, the integration of current agent technologies into the standardized MEC system is discussed. Lastly, a case study is presented on how to integrate an existing Internet of Things agent framework and agent-based edge application seamlessly to the MEC system.
This paper presents a distributed software mobile agent framework for media access. The
access and delivery of media is enhanced by an agent architecture framework that
utilizesmobile agent characteristics to provide access and delivery of media. Migrating agents
to the media source and destination devices enables the framework to discover the properties of
devices and media within networks dynamically. The mobile agents in this framework make
decisions and work together to enable access and delivery of media through the transcoding of
media based on the properties of the data path and device constraints.
Introductory tutorial on the foundations of agents and multi-agent systems at the 18th European Agent Systems Summer School (EASSS 2016) – 25 July 2016, Catania, Italy
Distributed Artificial Intelligence with Multi-Agent Systems for MECTeemu Leppänen
In this paper, the Multi-access Edge Computing (MEC) system architecture, as defined by the ETSI standards, is modeled as a multi-agent system. MEC system management services and application execution components are designed as software agents, facilitating distributed artificial intelligence capabilities in their operation and cooperation. Further, the integration of current agent technologies into the standardized MEC system is discussed. Lastly, a case study is presented on how to integrate an existing Internet of Things agent framework and agent-based edge application seamlessly to the MEC system.
This paper presents a distributed software mobile agent framework for media access. The
access and delivery of media is enhanced by an agent architecture framework that
utilizesmobile agent characteristics to provide access and delivery of media. Migrating agents
to the media source and destination devices enables the framework to discover the properties of
devices and media within networks dynamically. The mobile agents in this framework make
decisions and work together to enable access and delivery of media through the transcoding of
media based on the properties of the data path and device constraints.
Introductory tutorial on the foundations of agents and multi-agent systems at the 18th European Agent Systems Summer School (EASSS 2016) – 25 July 2016, Catania, Italy
This presentation deals with Multi Agent Systems and their application in industry and research. The presentation has beenmade by Zahia Guessoum (www-poleia.lip6.fr/~guessoum) Maître de Conférence at the Université Pierre et Marie Curie and me.
We are providing training on IEEE 2016-17 projects for Ph.D Scalars, M.Tech, B.E, MCA, BCA and Diploma students for
all branches for their academic projects.
For more details call us or watsapp us @ 7676768124 0r 9545252155
Email your base papers to "adritsolutions@gmail.co.in"
We are providing IEEE projects on
1) Cloud Computing, Data Mining, BigData Projects Using JAva
2) Image Processing and Video Procesing (MATLAB) , Signal Processing
3) NS2 (Wireless Sensor, MANET, VANET)
4) ANDRIOD APPS
5) JAVA, JEE, J2EE, J2ME
6) Mechanical Design projects
7) Embedded Systems and IoT Projects
8) VLSI- Verilog Projects (ModelSim and Xilinx using FPGA)
For More details Please Visit us at
Adrit Solutions
Near Maruthi Mandir
#42/5, 18th Cross, 21st Main
Vijaynagar
Bangalore.
Software Agents are very useful in coming Software development process. This ppt discuss introduction and use of Agents in Software development process.
Privacy preserving relative location based services for mobile usersLeMeniz Infotech
Privacy preserving relative location based services for mobile users
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
A Survey of Privacy-Preserving Algorithms for Finding meeting point in Mobile...IJERA Editor
Location privacy in Location Based Services (LBS) is the capability to protect the connection between user’s identity, uncertainty sources, servers and database, thereby restraining an impending attacker from conveniently linking users of LBS to convinced locations. Smart Phones have become most important gadget for maintaining the daily activities, highly interconnected urban population is also increasingly dependent on these gadgets to regulate and schedule their daily lives. These applications often depend on current location of user or a class of user. Use of Smart Mapping technology is also increasing in large area; this system provides an easy attainable online platform that can be used for accessing many services. This survey paper projects the privacy-preserving algorithm to find the most favorable meeting location for a class of users. GSM calculates the location of all users.
Introduction to agents and multi-agent systemsAntonio Moreno
Multi-agent systems course at University Rovira i Virgili. Slides mostly based on those of Rosenschein, from the content of the book by Wooldridge.
Lecture 1-Introduction to agents and multi-agent systems.
LPM: A DISTRIBUTED ARCHITECTURE AND ALGORITHMS FOR LOCATION PRIVACY IN LBSIJNSA Journal
Recent advances in mobile communication and development of sophisticated equipments lead to the wide spread use of Location Based Services (LBS). A major concern for large-scale deployment of LBSs is the potential abuse of their client location data, which may imply sensitive personal information. Protecting location information of the mobile user is challenging because a location itself may reveal user identity. Several schemes have been proposed for location cloaking. In our paper, we propose a generic Enhanced Location Privacy Model (LPM), which describes the concept, the architecture, algorithms and the functionalities for location privacy in LBS. As per the architecture, the system ensures location privacy, without trusting anybody including the peers or LBS servers. The system is fully distributed and evaluation shows its efficiency and high level of privacy with QoS
software agents course details and methods of programming in jade.
A software agent is a persistent, goal-oriented computer program that reacts to its environment and runs without continuous direct supervision to perform some function for an end user or another program. Some, but not all, software agents have UIs (user interfaces). A software agent is the computer analog of an autonomous robot.
Software agents represent an evolutionary step beyond conventional computer programs. Software agents can activate and run themselves, not requiring input from or interaction with a human user. Software agents can also initiate, oversee, and terminate other programs or agents including applications and online intelligent agents.
Among a great many other applications, software agents:
Conduct targeted Internet searches.
Check and prioritize incoming e-mail.
Test new computer games.
Fill out e-forms.
Conduct online job searches.
Synchronize social networking profiles.
Assemble customized news reports.
Find good deals in e-commerce.
Software Agents & Their Taxonomy | Ecommerce BBA HandoutHem Pokhrel
Software Agents leacture handouts for eCommerce students of Prime College BBA Stream. This presentation will provide you the short overview of the rapidly evolving area of software agents and their classification with their applications and significance.
This presentation deals with Multi Agent Systems and their application in industry and research. The presentation has beenmade by Zahia Guessoum (www-poleia.lip6.fr/~guessoum) Maître de Conférence at the Université Pierre et Marie Curie and me.
We are providing training on IEEE 2016-17 projects for Ph.D Scalars, M.Tech, B.E, MCA, BCA and Diploma students for
all branches for their academic projects.
For more details call us or watsapp us @ 7676768124 0r 9545252155
Email your base papers to "adritsolutions@gmail.co.in"
We are providing IEEE projects on
1) Cloud Computing, Data Mining, BigData Projects Using JAva
2) Image Processing and Video Procesing (MATLAB) , Signal Processing
3) NS2 (Wireless Sensor, MANET, VANET)
4) ANDRIOD APPS
5) JAVA, JEE, J2EE, J2ME
6) Mechanical Design projects
7) Embedded Systems and IoT Projects
8) VLSI- Verilog Projects (ModelSim and Xilinx using FPGA)
For More details Please Visit us at
Adrit Solutions
Near Maruthi Mandir
#42/5, 18th Cross, 21st Main
Vijaynagar
Bangalore.
Software Agents are very useful in coming Software development process. This ppt discuss introduction and use of Agents in Software development process.
Privacy preserving relative location based services for mobile usersLeMeniz Infotech
Privacy preserving relative location based services for mobile users
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
A Survey of Privacy-Preserving Algorithms for Finding meeting point in Mobile...IJERA Editor
Location privacy in Location Based Services (LBS) is the capability to protect the connection between user’s identity, uncertainty sources, servers and database, thereby restraining an impending attacker from conveniently linking users of LBS to convinced locations. Smart Phones have become most important gadget for maintaining the daily activities, highly interconnected urban population is also increasingly dependent on these gadgets to regulate and schedule their daily lives. These applications often depend on current location of user or a class of user. Use of Smart Mapping technology is also increasing in large area; this system provides an easy attainable online platform that can be used for accessing many services. This survey paper projects the privacy-preserving algorithm to find the most favorable meeting location for a class of users. GSM calculates the location of all users.
Introduction to agents and multi-agent systemsAntonio Moreno
Multi-agent systems course at University Rovira i Virgili. Slides mostly based on those of Rosenschein, from the content of the book by Wooldridge.
Lecture 1-Introduction to agents and multi-agent systems.
LPM: A DISTRIBUTED ARCHITECTURE AND ALGORITHMS FOR LOCATION PRIVACY IN LBSIJNSA Journal
Recent advances in mobile communication and development of sophisticated equipments lead to the wide spread use of Location Based Services (LBS). A major concern for large-scale deployment of LBSs is the potential abuse of their client location data, which may imply sensitive personal information. Protecting location information of the mobile user is challenging because a location itself may reveal user identity. Several schemes have been proposed for location cloaking. In our paper, we propose a generic Enhanced Location Privacy Model (LPM), which describes the concept, the architecture, algorithms and the functionalities for location privacy in LBS. As per the architecture, the system ensures location privacy, without trusting anybody including the peers or LBS servers. The system is fully distributed and evaluation shows its efficiency and high level of privacy with QoS
software agents course details and methods of programming in jade.
A software agent is a persistent, goal-oriented computer program that reacts to its environment and runs without continuous direct supervision to perform some function for an end user or another program. Some, but not all, software agents have UIs (user interfaces). A software agent is the computer analog of an autonomous robot.
Software agents represent an evolutionary step beyond conventional computer programs. Software agents can activate and run themselves, not requiring input from or interaction with a human user. Software agents can also initiate, oversee, and terminate other programs or agents including applications and online intelligent agents.
Among a great many other applications, software agents:
Conduct targeted Internet searches.
Check and prioritize incoming e-mail.
Test new computer games.
Fill out e-forms.
Conduct online job searches.
Synchronize social networking profiles.
Assemble customized news reports.
Find good deals in e-commerce.
Software Agents & Their Taxonomy | Ecommerce BBA HandoutHem Pokhrel
Software Agents leacture handouts for eCommerce students of Prime College BBA Stream. This presentation will provide you the short overview of the rapidly evolving area of software agents and their classification with their applications and significance.
A Performance Analysis of Chasing Intruders by Implementing Mobile AgentsCSCJournals
An Intrusion Detection System in network fetches the intrusions information from systems by using Mobile Agents aid. Intrusion Detection System detects intrusions based on the collected information and routes the intrusion. The intelligent decisions on communications, permit agents to gain their goals more efficiently and provide more survivability and security of an agent system. The proposed model showed a formal representation of information assurance in agent messaging over a dynamic network by probability of redundant routes. The proposed Intrusion Detection System, chase intruders and collect information by the Mobile Agents. Our propose architecture is an information exchange method and chasing intrusion along with a method by implementing Mobile Agents.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering
We describe a novel approach and a software framework for mobile crowdsensing applications with mobile agents that enables energy efficient, robust and scalable campaign execution. The framework is Web-enabled for integration with existing pervasive computing systems.
Architecture for Intrusion Detection System with Fault Tolerance Using Mobile...IJNSA Journal
This paper is a survey of the work, done for making an IDS fault tolerant.Architecture of IDS that uses mobile Agent provides higher scalability. Mobile Agent uses Platform for detecting Intrusions using filter Agent, co-relater agent, Interpreter agent and rule database. When server (IDS Monitor) goes down, other hosts based on priority takes Ownership. This architecture uses decentralized collection and analysis for identifying Intrusion. Rule sets are fed based on user-behaviour or applicationbehaviour.This paper suggests that intrusion detection system (IDS) must be fault tolerant; otherwise, the intruder may first subvert the IDS then attack the target system at will.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Intelligent Buildings: Foundation for Intelligent Physical AgentsIJERA Editor
FIPA is an IEEE Computer Society standards organization that promotes agent-based technology and the interoperability of its standards with other technologies. In the design phase of Intelligent Buildings, it is essential to manage many services and facilities, to do this, multi-agent systems are a good tool to manage them. In this paper, we will gereneral description of the features and elements of multiagent systems described by Foundation for Intelligent Physical Agents (FIPA). Secondly, we will focus on the architectures of these multiagent systems. And finally, we will propose a multi-agent system design to see the application in the design of a detached house where the lighting, air conditioning and security systems will be integrated.
https://utilitasmathematica.com/index.php
Our journal has a actively working to create a more diverse editorial board that represents a wide range of perspectives and experiences within the statistics field. It encourages authors to consider JEDI aspects in their research and actively participates in outreach programs to attract and support a more diverse group of researchers.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
3. Introduction to Autonomous Mobile Agents 3
Network Computing Lab
Head:
Dr. Dimiter R. Avresky
Members:
Natcho Natchev
Juan Ramón Acosta
Yavutz Varoglu
Marin Marinov
Steve Frechet
Luke Demoraski
4. Introduction to Autonomous Mobile Agents 4
Our Interests
Optimization and Performance Analysis
Fault-Tolerance
Interconnection Networks
Distributed Parallel Systems & Network
Computers
Protocol Validation, Efficient and
Reliable Routing
Agent Applications
5. Introduction to Autonomous Mobile Agents 5
Where to apply Agents
Transparent Resource Allocation
System Fault Tolerance
System Performance Improvement
Dynamic Network Reconfiguration
6. Introduction to Autonomous Mobile Agents 6
Network Computing
Discipline that studies homogenous and
heterogeneous Multi-computers and
clusters that are built using readily
available components and System Area
Networks (SANs)
7. Introduction to Autonomous Mobile Agents 7
Reduce bandwidth usage
Reduce total completion time
Reduce latency
Continue when disconnected
Balance load
Dynamically deploy components
Reasons for Using Agents
8. Introduction to Autonomous Mobile Agents 8
Dataset
Dataset
Dataset
Dynamically
selected
proxy site
Merged and
filtered data
stream
Reason 1:
Reduce Bandwidth Usage
9. Introduction to Autonomous Mobile Agents 9
Dataset
Dataset
DatasetFact
• Sending an agent avoids remote interaction.
Goal
• Avoiding remote interaction leads to faster
completion times.
Current Systems
• Do not meet the goal in all network environments
• Tradeoff: Local interaction vs. interpretive
overhead
Reason 2: Reduce Total Time
10. Introduction to Autonomous Mobile Agents 10
Reason 3: Reduce Latency
Sumatra chat server
[RASS97]
1. Observe
high average
latency to
clients
2. Move to
better location
2 to 4 times
smaller latency
in trial runs
11. Introduction to Autonomous Mobile Agents 11
Reason 4: Disconnected Operation
Dataset
Dataset
Dataset
Dynamically
selected
proxy site
X
Agent continues its task even
if the link to its home machine
goes down (temporarily).
12. Introduction to Autonomous Mobile Agents 12
Reason 5: Load Balancing
Machine A Machine B
Machine A Machine B
Agent moves to
balance load
15. Introduction to Autonomous Mobile Agents 15
Origins
In 1970, Carl Hewitt proposed the “Actor Model”
Actor, a computational Agent with an address and
behavior that communicates with other via
message-passing
Based on Distributed Artificial Intelligence (DAI) and
Parallel Artificial Intelligence (PAI) theories and
formalisms
Systems based on Actors became Multi-Agent
Systems (MAS)
16. Introduction to Autonomous Mobile Agents 16
Definition
Agent is a software/hardware component that
cooperate with other agents, interacts with its
environment, learns, is autonomous and has a
social contract with its user
Requirements:
Cooperate with other Agents
Learn (Interact with its Environment)
Be Autonomous
17. Introduction to Autonomous Mobile Agents 17
Cooperate Learn
Autonomous
Smart
Agents
Collaborative
Agents
Interface
Agents
Collaborative
Learning
Agents
What is not an Agent
Anything outside the
intersecting areas is not an
Agent
Not Agents are: Expert
Systems, Knowledge Systems
and Distributed Processes
Agents operate at knowledge
level not symbol level
[Foner93] and Pattie Mae,
“Current commercially available
agents barely justify their name”
Figure 1. Agent Categories [Nwana95]
18. Introduction to Autonomous Mobile Agents 18
Collaborative Agents
Attributes:
Autonomous, Social Ability,
Responsiveness and Pro-
Activeness
Goals:
Create a system that
Interconnects other agents
to assemble a more complex
Function
Applications:
Distributed Sensor Networks,
Air Traffic Control and Enhance
Reliability
User 1 User 2 User 3
D-B1 D-B2 D-Bn
I-A1 I-A2 I-An
T-A1 T-A2 T-An
Proposed
SolutionTask
Layer 1
Layer 2
Infosphere
Figure 2. The Pleiades Architecture at
Carnegie Mellon University (CMU)
19. Introduction to Autonomous Mobile Agents 19
Interface Agents
Attributes:
Emphasis on autonomy and
Learning in order to perform
Tasks on behalf of the user
Goals:
Promote cooperation between
end users and software agents
Applications:
Assistants(Travelers,Schedulers)
Memory Aid, Filters, Match
Making, Buying or Selling on
behalf
Application
User A’s
Agent
User’s
Agent
User
Asking
User feedback
Programming by example
Figure 3. How Interface Agents Work
by Pattie Maes
Interacts
Interacts with
Communication
ObservesandImitates
20. Introduction to Autonomous Mobile Agents 20
Mobile Agents
Attributes:
Computational process capable
of roaming the network gathering
information on behalf of their
Owner to return later “home”
Goals:
Reduce communication costs,
Maximize local resources usage,
Create a flexible distributed
computing environment
Applications:
Personal intelligent
communicators, Emergency Alert
systems, Reconfigurable Mobile
Computing, Network Routing
A
A
Sensor
Sensor
A1
A1
Server
Email Notification
SMS Notification
Install Alarm
Figure 4. Whether Alarm System [Johansen99]
21. Introduction to Autonomous Mobile Agents 21
InformationInternet Agents
Attributes:
Manage or collate information from
Distributed sources and have
Knowledge where to look for and
find information
Goals:
Provide an expressive integrated
interface to the Internet
Applications:
Filtering Email, Meeting Schedulers,
System Maintenance, Newspapers
online.
User
Information
Agent Program
Lycos
Local cache
WebCrawler North Star Robot
Spider
Mite WAIS
URL Search
DBMS
World Wide
Web
Figure 5. WebBot running in Browser
[Nwana95]
22. Introduction to Autonomous Mobile Agents 22
Reactive Agents
Attributes:
Respond to stimulus generated
By the environment, manages
complex patterns that emerge
from this behavior
Goals:
Used to build systems with no
internal symbolic models and
Whose “smartness” derives from
interactions
Applications:
Physical Robots, Video games, Virtual
Worlds and Real-Time embedded
systems
Wonder
Avoid Obstacles
Explore
S
E
N
S
I
S
N
G
A
C
T
I
N
G
Figure 6. Brook’s Sumpton Architecture
[Nwana95]
23. Introduction to Autonomous Mobile Agents 23
Hybrid Agents
Attributes:
Agents constructed combining
one or more of the agent
types mentioned earlier.
Goals:
Maximize the strengths and
minimize deficiencies of some
techniques
Mental Model
World Model
Social Model
SG PS
SG PS
SG PS
Perception Communication Action
Agent KB Agent Central Unit
Cooperative Planing Layer (CPL)
Local Planing Layer (LPL)
Behavior Based Layer (BBL)
World Interface/Body
Figure 7. The InteRRaP Hybrid Architecture
[Nwana95]
24. Introduction to Autonomous Mobile Agents 24
Sociological View of Agents
According to Leonard N. Foner, Agents must have a
“Social Contract”
Contract must include:
Discourse
Risk and Trust
Graceful Degradation
Anthropomorphism
Expectation
Agents and their applications “need to be subject to
same behavioral analysis as human”
An example: Julia TinyMUD developed at Carnegie
Mellon University can start polemical discussions
26. Introduction to Autonomous Mobile Agents 26
Autonomous Mobile Agents
“ Components that operate without direct intervention of
humans, have control over its actions, internal state based
on knowledge and have the capacity to migrate from
machine to machine”
A
A
Sensor
Sensor
A1
A1
Server
Email Notification
SMS Notification
Install Alarm
Figure 8. Whether Alarm System [Johansen99]
27. Introduction to Autonomous Mobile Agents 27
Attributes
Behavior can be predicted using beliefs,
desires and rationality
Make choices based on what they know
about the world
Roam the network using knowledge
Create a flexible distributed environment
28. Introduction to Autonomous Mobile Agents 28
Agents as Intentional Systems
Agent behavior can be predicted using beliefs,
desires and rationality
Attitudes to represent an Agent
Information.- Refers to the information de agent has
about the world it occupies, .e.g. Belief and Knowledge
Pro-Attitudes.- Those that guide Agent Actions, e.g.
Desire, intention, obligation, commitment, etc.
Agents make choices and form intentions on the
basis they have about the world, e.g. “stock order
matching rules”
29. Introduction to Autonomous Mobile Agents 29
Representation of Intentions
Possible Worlds [Wooldrige94], semantic representation of
beliefs, knowledge. Goals are a set of states considered
possible
Uses classical prepositional logic extended with two new
operators: = necessarily and = possibly
Omniscience Problem, An Agent knows all proposition
tautologies then the agent knowledge is closed under
logical consequence
Alternatives to this problem: Belief and Awareness, and
Deduction Model
Meta-Languages [Wooldrige94], a syntactic representation
between a Meta-Term and Agent.
30. Introduction to Autonomous Mobile Agents 30
Mobile Agent Computing Model
Communication Infrastructure
(SCI, Myrinet, ServerNet, VIA, Ethernet, etc…)
Message-Passing
Subsystem
Agent Execution
Environment
Client Application
Environment
Message-Passing
Subsystem
Agent Execution
Environment
Client Application
Environment
RPC, JNI,MPI, PVM,CORBA
Tcl/tk, Java, C++
J.R.Acosta defines the three bottom layers as an Agent Service Broker
31. Introduction to Autonomous Mobile Agents 31
Agent Process Migration
Strong Mobility
Data, Code and
Control Block
Weak Mobility
Data and Code
Agent
Run
prc_cn
tl_blck
Ctrl
Blck
I
D
CtrlB
lck
Agent
Susp
I
D
CtrlB
lck
Ctrl
Blck
Agent
Run
Client Application Server Application
7. Resume
1. Migrate
2. Suspend
3.Generate
Bundle
4. Transmit
5. Receive
Bundle
6. Spawn
New
Process
6. Restore
State
8. Interact
Host A Host B
Agent
Susp
Code
Code
Figure 10. Process Migration
32. Introduction to Autonomous Mobile Agents 32
Advantages
Access resources locally and eliminates
transfer of intermediate data, incrementing
efficiency
Do not require permanent connections
Load Balance
Reduce Latency
Portable and secure (Uses interpreted
languages)
34. Introduction to Autonomous Mobile Agents 34
Aglets
• Java
• Weak mobility
• Event-driven programming model
(dispatch, onDispatching, onArrival, …)
• Persistent store
• “Proxies” for location transparency
• Machine protection
http://www.trl.ibm.co.jp/aglets/IBM
35. Introduction to Autonomous Mobile Agents 35
Jumping Beans
Central
Domain
Server
Agency
“Mini-server”
Agency Agency
Jump
through
central
server
• Java
• Weak mobility
• Central server for tracking,
managing and authenticating
agents (but also failure point
and bottleneck)
• Persistent store
• Machine protection
http://www.JumpingBeans.com/
Ad Astra Engineering
36. Introduction to Autonomous Mobile Agents 36
Voyager
• Java
• Built on top of CORBA
• Weak mobility
• Persistent store
• Federated directory service and group communication
(multicast)
• Machine protection
ObjectSpace
http://www.objectspace.com/products/
37. Introduction to Autonomous Mobile Agents 37
Tacoma
• C, Tcl/Tk, Scheme, Python, Perl (public
release), several more internally
• Weak mobility
• Single, simple abstraction: meet
– Easy to add a new language
– Less opportunity for optimization
• Machine protection
University of Tromsø / Cornell University
http://www.tacoma.cs.uit.no/
38. Introduction to Autonomous Mobile Agents 38
D’Agents (a.k.a Agent Tcl)
Started by Robert
Gray in Spring, 1994
Only system with
strong mobility
Multiple Languages
Tcl, Java and Schema
Support Services
Directory service
Tracker
Mobile Computing
Performance
Improving
Communication and
Migrations are
Expensive
Security
Machine Protection
Agent Protection in
Transit
No Agent Protection
while on a Machine
39. Introduction to Autonomous Mobile Agents 39
D’Agents: Architecture
Figure 11. Agent Tcl Architecture [Gray97]
Transport (TCP/IP)
Server Engine
Java
VM
Scheme
Interp
Tcl
Interp
Agents
VM / Interpreter
Security
State
Capture
VM
Server
stubs
40. Introduction to Autonomous Mobile Agents 40
D’Agents: Example
Machine Z
Parent
Machine A
ChildChild
1. Submit
child
...
2. Jump
3. Send
results
41. Introduction to Autonomous Mobile Agents 41
D’Agents: Tcl Programming
proc child {machines} {
global agent
# migrate through machines
set results {}
foreach machine $machines {
# do task, update results
agent_jump $machine
}
# send back results and end
agent_send
$agent(root) 0 $results
agent_end
}
agent_begin
# submit child
set machines {A B …}
agent_submit
$agent(local-server)
-procs child
-vars machines
-script {child $machines}
# get results
agent_receive
code results -blocking
puts $results
agent_end
Child Agent Parent Agent
42. Introduction to Autonomous Mobile Agents 42
D’Agents: Java Programming
// Parent Agent
// register with the agent system
Agent a = new Agent();
AgentId id = a.begin (10); // timeout after 10 seconds
// submit the child agent
Vector machines = new Vector();
machines.addElement (new String (“A”));
machines.addElement (new String (”B"));
ChildAgent childAgent = new ChildAgent (machines);
AgentId childId = a.submit (“localhost”, childAgent, 10);
// wait for and display the result and then end
RecMessage result = a.receive (10);
System.out.println (result.getMessage());
a.end (10);
43. Introduction to Autonomous Mobile Agents 43
D’Agents: Java Programming
(Cont…)
class ChildAgent extends AgentEntryPoint {
private Vector m_machines; // machine list
public ChildAgent (Vector machines)
{ m_machines = machines; }
public void run (Agent a) {
String results = "";
// migrate through all the machines in the list
for (int i = 0; i < m_machines.size(); ++i) {
//do tasks, update results
String machine = (String) m_machines.elementAt (i);
a.jump (machine, 10);
}
// send back result and exit
Message message = new Message (0, results);
a.send (a.getRootId(), message, 10); a.end (10);
}
}
45. Introduction to Autonomous Mobile Agents 45
What Lowers Performance?
1. All messages through server (plus TCP/IP)
Server
Machine A Machine B
TCP/IP
connection Server
Machine A
Server
Tcl
Interp
TCP/IP
connection
jump
2. Interpreter initialization (plus TCP/IP)
46. Introduction to Autonomous Mobile Agents 46
D’Agents: Security
Main concerns
Protect the Machine from an Agent
Protect Agents
Protect Group of Machines
Authentication
RSA public-key cryptography to authenticate
agent’s owners
Pretty Good Privacy (PGP) algorithm for digital
signatures and encryption for Agent migration and
communication
47. Introduction to Autonomous Mobile Agents 47
Filesystem
Manager
D’Agents: Protecting the Machine
Server
kernel
Security
Files
Tcl Agent
(digitally signed)
1. Authenticate
2. Accept or reject
3. Resume execution
4. open tutorial.ppt r
5. Access request (read)
and security vector
{owner, untrusted machines?}
6. Yes / no /
quantity
7. If yes,
open
Tcl
Interpeter
48. Introduction to Autonomous Mobile Agents 48
Going Forward
Improve access restrictions and
resource scheduling for most
application environments
Remote communication as fast as RPC
Just-in-Time compilation with code
cashing
51. Introduction to Autonomous Mobile Agents 51
Agent Oriented Software
Engineering
Specifies that a complex system can be
represented as a hierarchy
Agents are:
Problem solving entities in an environment
Fulfill a specific role
Control both their internal state and behavior
Adopt goals and take initiative to satisfy their
design objectives
52. Introduction to Autonomous Mobile Agents 52
Agent Oriented Software
Cycle
Implementation:
Execute,
Compile
Verification:
Axiomatic
&
Semantic
Specification:
Beliefs, goals,
actions and
continuous interaction
53. Introduction to Autonomous Mobile Agents 53
Intelligent Software Engineering
[Deugo99]
Concerned with the creation of principles to
describe intelligent software patterns
Combine Patterns and Artificial Intelligence
Techniques
Produce Complex and Intelligent Software
Products
Agent Patters are Intelligent Software
Patterns
54. Introduction to Autonomous Mobile Agents 54
Design Patterns
Created by Christopher Alexander. Applied to
Architecture
Relation between a problem, its context and the
solution
Format:
Name .- Descriptive name for the pattern
Problem.- Clear description of the problem to be solved
Context.- Situation when the pattern might be applied
Forces .- Items that restrict or influence when to apply
the pattern
Solution.- Solution in the context that balances the forces
55. Introduction to Autonomous Mobile Agents 55
Agent Design Patterns
Architectural Patterns, describe agent system
architecture
Interaction Patterns, describe how agents
communicate with each other
Agent Mobility Patterns, describe how agent
roam the network
56. Introduction to Autonomous Mobile Agents 56
Architectural Patterns
Name:
Layered Agent Pattern
Problem:
What software architecture best
supports the behavior of agents
Forces:
Agent system spans several levels of
abstraction
Software must include all aspects of
agency
Be able to address simple and
sophisticated agent behavior
Solution:
Agents and the system needs are
discomposed into six layers
Mobility
Collaboration
Actions
Reasoning
Beliefs
Sensory
Figure 13. Layered Agent pattern,
[Kendall98]
57. Introduction to Autonomous Mobile Agents 57
Interaction Patterns
Direct Coupling
Point to point, Ack/Req, Agents do not move
Proxy Agent
Communication Session
Badges
Event Dispatcher
58. Introduction to Autonomous Mobile Agents 58
Proxy Agent Pattern [Deugo99]
Name:
Proxy Agent Pattern
Problem:
If the Agent sending a message does not
expect the receiving Agent to move or
Direct Coupling pattern is not applicable
for performance reasons, how do mobile
agent communicate with others ?
Forces:
Mobile Agents change often position
Communication peer-to-peer
Required communication with static
Agents
Client Agent
Proxy Agent
Server Agent
Figure 14. Proxy Agent pattern
Solution: When Agent moves
away, agent creates proxy
Agent at its home location
59. Introduction to Autonomous Mobile Agents 59
Badges Pattern [Deugo99]
Name:
Badges Pattern
Problem:
How can an Agent find a suitable
communicating Agent without
specifying a concrete Agent?
Forces:
Communicating Agents are not known
in advance and can change
Design must be flexible and general
After identifying communicating agents
communication should be direct
Solution:
Attach badges to agents. A place provides
a service to find a local agent
carrying certain badge.
Badge C
Badge B
Badge A
Look for Badge B
Figure 15. Badges Pattern
After finding an available
agent communication starts
60. Introduction to Autonomous Mobile Agents 60
Agent Mobility Patterns
By Passer
Commuter
Interface
Isolator
Monitor
Rover
61. Introduction to Autonomous Mobile Agents 61
By Passer Pattern [Hung2002]
Agents
Used to avoid unreliable
links
Works on behalf of the
client
When finished it returns
to home
If an Agent does not
return the pattern is
then called Commuter
Serve
r
AgentClient
Figure 16. By Passer Pattern
62. Introduction to Autonomous Mobile Agents 62
Interface Pattern [Hung2002]
Agents
Act as intermediary
between a client and a
server
Execute anywhere
All communication
between client and
server goes through the
Agent
If simple messaging
goes through the Agent
pattern is called Monitor
Server
AgentClient
Figure 17. Interface Pattern
63. Introduction to Autonomous Mobile Agents 63
Rover Pattern [Hung2002]
Agents
Visits several sites
in sequence using
an itinerary
Streamline recovery
model
Helps to reduce
network congestion
Server
AgentClient
Server
Agent
Server
Agent
Agent
Figure 18. Rover Pattern
64. Introduction to Autonomous Mobile Agents 64
Performance of Agent
Patterns
Stock
Server
Roving
Agent
Roving
Agent
Roving
Agent
Static
Agent
Trader Host Roving Trader
Success Rate
95% Confidence
Interval
Host A 50.8% ±1.4%
Host B 100.0% ±0.0%
Host C 100.0% ±0.0%
Host A
Host B
Host C
Figure 19. Trading
System
[Hung2002]
Agents compete
to match the best
sale order of an
specific Stock
Symbol
65. Introduction to Autonomous Mobile Agents 65
Case Study
Current Architecture
Pattern Identification
Agent Architecture
Self-Organized Multi-Modular Robotic
Control
Dr. José Negrete-Martínez
Facultad de Física e Inteligencia Artificial
Universidad Veracruzana, México
Figure 20. Modular Robot [Negrete 2002]
66. Introduction to Autonomous Mobile Agents 66
Case Study: Current Architecture
Motor(j)
B
DS Rotating(j)
m Payoff(j)
IRS AFD AD
n
Motor(j)
B
DS Probing(j)
Payoff(i)IRS AFD AD
SoftwareMechatronic
IRS .- Infrared Square Wave
DS .- Computer-servo’s interface
AFD.- Amplifier-Filter-Detector Circuit
AD.- Interface between AFD and
Computer
DS.- Computer-servo’s Interface
m,n .- Memory Register
Payoff(k).- function that calculates the sign of the difference
of light intensity before and after the module’s motor action
Decision(k).-function that adds a signed step to the present
position of the Motor(i). The Decision(i) function calls the
Payoff(i) function
Figure 21. Modular Architecture
[Negrete 2002]
67. Introduction to Autonomous Mobile Agents 67
Case Study: Pattern Identification
Layered Agent Pattern, Use of Agent System that
supports mobility
Monitor Pattern, Data Acquisition Agent in charge of
receiving Infra Red Signal and determining variation
on light intensity
Commuter Pattern, Action Agents in charge of
executing the ‘Two Arm Bandit’ learning algorithm that
inputs the Payoff value and invoking and Orientation
Agent
Event Dispatcher Pattern, Action Agent to report step
motor movement
Interface Pattern, Motor Driver Agents that interfaces
with servo motor interface and a Action Agent
68. Introduction to Autonomous Mobile Agents 68
Desition
Agent
Case Study: Agent Architecture
Motor Driver
Agent
Action
Agent
SoftwareMechatronic
Orientation
Agent
DS
IRS AFD AD
Motor
moveMotorEvent
executeOtherAction
makeDesition
determineOrintation
haltActioEvent
newReading
stepMotor
Figure 10. Agent Architecture
Data
Aquisition
Agent
69. Introduction to Autonomous Mobile Agents 69
Case Study: Results
Architecture is scalable and distributed
Allows parallel continuous activity
Remains Modular and better separation of
concerns
Abstracts hardware Interfaces, more generic
Enables System for Distributed Network or
Grid Computing
70. Introduction to Autonomous Mobile Agents 70
Conclusions
Agent technology promises big improvements
over traditional technologies and positions
itself as the future technology for complex software
systems.
However, as any other technology, it is only
good if it profs itself useful and people is willing
to spend time with it to solve problems
Otherwise it is a nice try
Editor's Notes
These six reasons are discussed at greater length in Section 2 of the paper that I will hand out after the tutorial.
The Sumatra chat server is presented in [RASS97] M. Ranganthan, Anurag Acharya, Shamik Sharma and Joel Saltz. Network-aware Mobile Programs. In Proceedings of the 1997 Usenix Technical Conference , pages 91-104, 1997. and also mentioned in Section 2.2.3 of the paper.
In this block of the seminar It will be defined what is a Software Agent What are the attributes and types of Agents
Speaker Notes: Agent technology can be traced back to 1970 Carl Hewitt proposed a model based on Actors. Actors, are objects self-contained, interactive and concurrently executing Gradually actor based systems became multi-agent systems as known today
Speaker Notes: The term agent has been misused by publishers, scientists and the industry. This obscures the technology real attributes, benefits and issues The community had set a minimum requirements that must be satisfied by any application
Speaker Notes: Considering that each requirement overlaps each other at the same point, the resulting intersecting areas represent all the categories of agents possible Anything outside is not an Agent Experts systems.- Are autonomous, but not cooperate or learn Knowledge systems .- Are passive and not autonomous Distributed Process, such as MPI, Java, JNI can move and cooperate but do not have knowledge and are not autonomous. Objects (C++, Java, Eiffel) are not agents. Agents can be built upon objects
Speaker Notes: Pleides is a distributes collaborative agent architecture with two layers. Layer 1. Task-specific collaborative agents (T-A) work on behalf of the user Layer 2. Information collaborative agents (I-A) that provide information to Layer 1 agents Interface agents get their data from database agents
Speaker Notes: Virtual environments Full body interaction between humans and a virtual world inhabitant agents Stock Trading
Speaker Notes: Can be used in reconfigure mobile computing A niche for this type of application is in wireless applications Network traffic management and QoS.
Speaker Notes: Can be used in reconfigure mobile computing A niche for this type of application is in wireless applications Network traffic management and QoS.
Speaker Notes: Brooks in 1991 defined the famous Sumptom architecture Does not maintain Symbolic models of the real world No planned behavior Reactive agents are viewed as a collection of modules that interoperate autonomously
Speaker Notes: Example uses a mix of reactive agents and collaborative agents BBL, contains a set of patterns of behavior describing the agent reactive skills LPL, is goal oriented CPL, enables agents to plan and cooperate to achieve multi-agent plans This architecture has being used on and autonomous robotic system
Speaker Notes: We should ask about an agent Is the agent behaving appropriately in its social context? It is causing social stress? It is a good citizen Example Julia a TinyMUD built in CMU Julia abilities were maintain conversations with MUD users, keep links to rooms and statistics Julia in a session was perceived by a human female as a boring human centered in Hockey
Speaker Notes: This block of the seminar Discuses what are Autonomous-Mobile Agents, their attributes and how they work
Speaker Notes: Autonomous Agents cooperate,learn and are proactive
Speaker Notes: It is coherent to treat an stock order matching component as a cooperative agent with the capability of finding the best priced order when it beliefs that we want to sell/buy to profit and not otherwise. Entering an order to sell/buy is our way to communicate our desires to the system.
Speaker Notes: It is coherent to treat an stock order matching component as a cooperative agent with the capability of finding the best priced order when it beliefs that we want to sell/buy to profit and not otherwise. Entering an order to sell/buy is our way to communicate our desires to the system.
Speaker Notes:
Speaker Notes: Can be used in reconfigure mobile computing A niche for this type of application is in wireless applications Network traffic management and QoS.
Speaker Notes: Strong Mobility More convenient for the agent programmer Subsumes weak mobility Weak Mobility Sufficient for all but load-balancing applications Well suited to the event-driven style of many agents Much less work for the system developer Supported by standard Java virtual machines
Speaker Notes: This block of the seminar Discuses what are Autonomous-Mobile Agents, their attributes and how they work
This graph shows the base performance of the D’Agents system (migration and messages) relative to a raw TCP/IP connection. A similar graph appears in the paper, but shows the base performance for Tcl , rather than Java, agents.
Speaker Notes: This block of the seminar Discuses what are Autonomous-Mobile Agents, their attributes and how they work
Speaker Notes: Agent Oriented Software Engineering Are a techniques for analyzing, designing and building complex systems Intelligent Software Engineering Describe the need for developing formalism for the design of agent based applications Agent Design Patterns The architectural patterns describe those patterns that have to do with the modeling of enabling agent systems, interaction between agents, and knowledge acquisition among other. Mobile Agent Patterns describe different mobility patterns an agent can follow, some performance results are presented to proof the benefits of using agent mobility.
The system is composed of inter-related sub-systems each of which is in itself a hierarchy
Speaker Notes: After several years researching how to create enabling Agent platforms, now researchers are focusing on new software engineering principles for the creation of Agent application
Speaker Notes: Design patterns are modern object oriented design methodology that was originated in civil engineering. The goal of a design pattern is to represent a relation between a problem its context and a solution. To realize the benefits promised using Agent technology it is necessary to have a repository of well defined design patterns.
Speaker Notes:
Speaker Notes:
Speaker Notes:
Speaker Notes:
Speaker Notes:
Speaker Notes: Items in blue are discussed in detail. The others are particular cases of them or not to relevant
Speaker Notes:
Speaker Notes:
Speaker Notes:
Speaker Notes: The goal of each trader was to buy a certain stock at the same price, before the opponent. One trader is static on Host A and a roving agent starts at A with an itinerary
Speaker Notes: This project was presented at the International Symposium on Robotics and Automation. September 1-4, 2002