This document provides an introduction to agent-based systems, including definitions of key concepts like agents, intelligent agents, software agents, and multi-agent systems. It discusses the characteristics of agents that distinguish them from regular software and provides examples of application domains for agent-based systems. Finally, it outlines several popular agent development kits and frameworks like JADE, Jason, Cougaar, and ABLE that can be used to build agent-based applications.
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
What is Intelligent agent, Abstract Intelligent Agents, Autonomous Intelligent Agents, Classes of intelligent agents, Application of an intelligent agent, Capabilities of an intelligent agent, Limitations of an intelligent agent.
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
What is Intelligent agent, Abstract Intelligent Agents, Autonomous Intelligent Agents, Classes of intelligent agents, Application of an intelligent agent, Capabilities of an intelligent agent, Limitations of an intelligent agent.
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.
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
Introduction to Agents and Multi-agent Systems (lecture slides)Dagmar Monett
Online lecture at the School of Computer Science, University of Hertfordshire, Hatfield, UK, as part of the 10th Europe Week from 3rd to 7th March 2014.
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
Types Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/y5swZ2Q_lBw
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "Types Of Artificial Intelligence" will help you understand the different stages and types of Artificial Intelligence in depth. The following topics are covered in this Artificial Intelligence Tutorial:
History Of AI
What Is AI?
Stages Of Artificial Intelligence
Types Of Artificial Intelligence
Domains Of Artificial Intelligence
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What Is The Difference Between Weak (Narrow) And Strong (General) Artificial ...Bernard Marr
Did you know there are two forms of artificial intelligence? Weak or narrow artificial intelligence (AI) is what we encounter daily today. Strong or general AI is the next phase where machines can think like humans without being programmed by humans. Learn the differences between weak and strong AI.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
A presentation on AI, Artificial Intelligence.
Intro of the Author
Automation vs AI
What is AI
History& Trends
Framework of Agents
Ethics
Social Economic Implications
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
Software Agents are very useful in coming Software development process. This ppt discuss introduction and use of Agents in Software development process.
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.
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
Introduction to Agents and Multi-agent Systems (lecture slides)Dagmar Monett
Online lecture at the School of Computer Science, University of Hertfordshire, Hatfield, UK, as part of the 10th Europe Week from 3rd to 7th March 2014.
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
Types Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/y5swZ2Q_lBw
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "Types Of Artificial Intelligence" will help you understand the different stages and types of Artificial Intelligence in depth. The following topics are covered in this Artificial Intelligence Tutorial:
History Of AI
What Is AI?
Stages Of Artificial Intelligence
Types Of Artificial Intelligence
Domains Of Artificial Intelligence
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Facebook: https://www.facebook.com/edurekaIN/
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LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
What Is The Difference Between Weak (Narrow) And Strong (General) Artificial ...Bernard Marr
Did you know there are two forms of artificial intelligence? Weak or narrow artificial intelligence (AI) is what we encounter daily today. Strong or general AI is the next phase where machines can think like humans without being programmed by humans. Learn the differences between weak and strong AI.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
A presentation on AI, Artificial Intelligence.
Intro of the Author
Automation vs AI
What is AI
History& Trends
Framework of Agents
Ethics
Social Economic Implications
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
Software Agents are very useful in coming Software development process. This ppt discuss introduction and use of Agents in Software development process.
leewayhertz.com-Auto-GPT Unleashing the power of autonomous AI agents.pdfKristiLBurns
Autonomous agents in artificial intelligence refer to systems or entities that can perceive their environment, make decisions and take actions to achieve specific goals without direct human intervention. These agents are designed to operate independently and adapt to environmental changes. They are commonly used in various applications, such as robotics, computer games, natural language processing and self-driving cars.
Introduction–Definition - Future of Artificial Intelligence – Characteristics of Intelligent Agents– Typical Intelligent Agents – Problem Solving Approach to Typical AI problems.
Hi there.
This presentation is made by me for a presentation in my university.
If you like my work do download and use please just not try to copy paste the content because if you're learning then learn from your heart.
Have great day!
Yay.... :)
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
Building Your Own AI Agent System: A Comprehensive GuideChristopherTHyatt
Building an AI Agent involves creating a computer system that can make decisions, choose tools, and take actions to achieve specific goals autonomously.
Slide ini merupakan pengenalan Disciplined Agile toolkit dan Disciplined Agile Delivery yang merupakan sekumpulan praktik agile yang bisa digunakan sebagai panduan dalam membangun software
Buku bebas karya saya tentang pengembangan aplikasi cloud computing menggunakan Node.js. Lisensi: CC-BY-SA. Kode sumber (LaTeX) serta kode sumber dari program2 yang ada di buku ini bisa diperoleh di akun github saya: https://github.com/bpdp/buku-cloud-nodejs
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
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.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
By Design, not by Accident - Agile Venture Bolzano 2024
Agent-based System - Introduction
1. Introduction to Agent-based
System
Bambang Purnomosidi D. P.
http://bpdp.name
This material was taken from my website. Have a look here:
http://bpdp.name/content:book:agent-based-
system:introduction:start
2. Agenda
● Definition and related concepts
● Agent and Regular Software
● Application of Agent-based System
● Organization related to Agent-based
System.
● Agent Development Kit
3. Agenda 1: Definition and
Related Concepts
● An agent is not necessarily related to
computer system.
● Here we will discuss only about
(intelligent) software agent
4. Agent Definition
● Agent is taken from the latin word agere,
means to do.
● Agent in computer science and industry
basically almost has the same
understanding with definition in the real
world, only in computer science it
refers to a software entity while in the
real world it refers to person,
instrument, something, or any other
object
5. Agent Definition (cont.)
● Merriam-Webster online dictionary:
– One who is authorized to act for or in the
place of another.
● An agent in computer science refers to a
software or other computational
entities which has intelligence
characteristics and can decide and act
based on its intelligency and other
information taken from its environment.
An agent usually acts on behalf of
computer user.
6. Agent Definition (cont.)
● An agent is anything that can be viewed as perceiving
its environment through sensors and acting upon
that environment through actuators (Russel and
Norvig, 2010).
● An agent is something that acts in an environment,
interact with the environment with a body, receive
information through their sensors, and act in the
world through their actuators, also called effectors
(David Poole and Alan Mackworth, 2010)
● An agent is a computer system that is situated in some
environment, and that is capable of autonomous
action in this environment in order to meet its design
objectives (M. Wooldridge and N.R. Jennings, 1995).
7. Agent and Artificial Intelligence
● According to Mc Carthy (1956, rewriten
again in 2007), AI is ”the science and
engineering of making intelligent
machines”.
● Agent is central in AI for obvious reasons.
AI does always try to make thing which
is intelligent. This thing is not necessary
a machine and it can be considered as
agent. Therefore we can conclude that
agent is the ultimate objective of AI.
8. Intelligent Agent
● An intelligent agent is an agent with
intelligent features. A system consists
of hardware and software, has
intelligent features, is the one that
usually called intelligent agent. This is
the closest in meaning with agent
defined by AI. If the agent is a software
then it is called intelligent software
agent.
9. Software Agent
● A software agent is a software who will
act on behalf of other party (in this
case, the party is computer user). To
act on behalf of other party, a software
agent needs to be intelligent enough,
so the term ”software agent” can also
be used interchangeably with
”intelligent software agent” although
people often called it just ”software
agent”.
10. Autonomous Agent
● An agent can also be seen from one of its
characteristics: autonomy. An agent is capable
to reveice comamand from computer users (i.e.
human who want to finish some task), and can
act intelligently to do those task(s) which has
been delegated by computer users. During his
activities, an agent basically can interact with
the environment, learn and then using its
knowledge to do its task without much
interaction and command from computer users.
This shows us that an agent has some degree of
autonomy. An intelligent agent which has
autonomy is called autonomous agent.
11. Mobile Agent
● A mobile agent basically also a software
agent. It has the same features and
characteristics as software agent with
an added capability: ”mobility”. A
mobile agent is software, together with
data, which can be executed in a
certain host to do a task and then move
to another host to continue its
execution. This mobility makes this kind
of software agent is called mobile
agent.
12. Multi-agent System /
Distributed Artificial Intelligence
● Some problems maybe too hard to be
solved by an agent alone. If an agent
can not solve a problem alone, it will
needs more agents to interact,
commnicate, and cooperate to solve
that problem. This situation is known as
multi-agent system (MAS).
15. Agenda 2: Agent and Regular
Software (Non-agent Software)
● Characteristics of software agent:
– Franklin and Graesser, 1996
16. Agent and Regular Software
(Non-agent Software)
● Jenning and Wooldridge, 1995:
– Autonomy: agents should be able to perform the majority of
their problem solving tasks without the direct intervention of
humans or other agents, and they should have a degree of
control over their own actions and their own internal state.
– Social ability: agents should be able to interact, when they
deem appropriate, with other software agents and humans in
order to complete their own problem solving and to help others
with their activities where appropriate.
– Responsiveness: agents should perceive their environment
(which may be the physical world, a user, a collection of
agents, the INTERNET, etc.) and respond in a timely fashion to
changes which occur in it.
– Proactiveness: agents should not simply act in response to their
environment, they should be able to exhibit opportunistic, goal-
directed behaviour and take the initiative where appropriate.
–
17. Agenda 3: Application of Agent-
based System
● Wooldridge (2002):
– Distributed systems: an agent become
a part of distributed system, as a
processing node.
– Personal software assistants: an agent
play the role of proactive assistants to
users working with some application.
18. Application of Agent-based
System
● Some notable application domain of
software agent (Wooldridge, 2002):
– Agents for workflow and BPM
– Agents for distributed sensing
– Agents for information retrieval and management
– Agents for e-commerce
– Agents for human-computer interfaces
– Agents for virtual environments
– Agents for social simulation
– Agents for industrial systems management
– Agents for spacecraft control
19. Agenda 4: Organizations
Related to Agent
● FIPA (http://www.fipa.org)
– FIPA (The Foundation for Intelligent
Physical Agent) is an IEEE Computer
Society standards organization that
promotes agent-based technology and
the interoperability of its standards with
other technologies.
20. Organizations Related to Agent
● European Software-Agent Research
Center
– The European Software-Agent Research
Center is an organization of software
agent research community in Europe.
People may join for free by e-mail the
webmaster.
– http://www.software-agent.eu/
21. Organizations Related to Agent
● AgentLink (http://www.agentlink.org)
● AgentLink is Europe's IST-funded Coordination
Action for agent-based computing. As such,
AgentLink coordinates research and
development activities in the area of agent-
based computer systems on the behalf of the
European Commission. AgentLink supports a
range of activities aimed at raising the profile,
quality, and industrial relevance of agent
systems research and development in Europe,
and promoting awareness and adoption of agent
technologies.
22. Organizations Related to Agent
● The World Wide Web Consortium (
http://www.w3.org)
– The World Wide Web Consortium (W3C)
is an international community that
develops standards to ensure the long-
term growth of the Web. The W3C
mission is to lead the World Wide Web
to its full potential by developing
protocols and guidelines that ensure
the long-term growth of the Web.
23. Agenda 5: Agent Development
Kit
● ABLE (Agent Building and Learning
Environment) -
http://www.alphaworks.ibm.com/tech/a
ble
– ABLE () is Java framework, component
library, and productivity tool kit for
building intelligent agents using
machine learning and reasoning.
Although no formal announcement, last
update was July 19, 2005, which is the
sign of unmaintained software.
24. Agent Development Kit
● Cougaar (http://www.cougaar.org)
– Cougaar is a Java-based architecture for the
construction of highly scalable distributed agent-
based applications. Cougaar includes an advanced
core architecture and a variety of of components
that simplify the development, visualization, and
management of complex distributed applications.
The Cougaar architecture includes components to
support agent-to-agent messaging, naming,
mobility, blackboards, external UIs, and additional
(pluggable) capabilities. Developer write
components, also called “plugins”, which are loaded
into agents to define their behavior. The Cougaar
Component Model allows the developer to configure
Cougaar to match both their domain and system
requirements / constraints.
25. Agent Development Kit
● FAMOJA (Framework for Agent-based
MOdelling with JAva) is software
framework consists of a collection of
Java classes which aid in the rapid
prototyping of agent-based model.
● http://www.usf.uos.de/projects/famoja/
● Features:
– A graphical user interface where models can easily be
run, examined, modified and rerun.
– Ready to use Agents for displaying data in charts
– Agents and Viewers for visualizing models where
Agents are situated in a grid environment
26. Agent Development Kita
● Janus (http://www.janus-project.org/Home) is
an enterprise-ready open-source multi-agent
platform fully implemented in Java 1.6. Janus
enables developers to quickly create web,
enterprise and desktop multiagent-based
applications. It provides a comprehensive set
of features to develop, run, display and
monitor multiagent-based applications.
Janus-based applications can be distributed
across a network.
27. Agent Development Kit
● Jason (http://jason.sourceforge.net/) is
an interpreter for an extended version
of AgentSpeak. It implements the
operational semantics of that language,
and provides a platform for the
development of multi-agent systems,
with many user-customisable features.
Jason is available Open Source, and is
distributed under GNU LGPL
28. Agent Development Kita
● JADE (Java Agent DEvelopment Framework -
http://jade.tilab.com/) is a software Framework fully
implemented in Java language. It simplifies the
implementation of multi-agent systems through a middle-
ware that complies with the FIPA specifications and
through a set of graphical tools that supports the
debugging and deployment phases. The agent platform
can be distributed across machines (which not even need
to share the same OS) and the configuration can be
controlled via a remote GUI. The configuration can be
even changed at run-time by moving agents from one
machine to another one, as and when required. JADE is
completely implemented in Java language and the
minimal system requirement is the version 1.4 of JAVA
(the run time environment or the JDK).
29. Agent Development Kita
● JIAC (Java-based Intelligent Agent
Componentware) is a Java-based agent
architecture and framework that eases
the development and the operation of
large-scale, distributed applications and
services. This library consists of
already-prepared services,
components, and agents which can be
integrated into an application in order
to perform standard tasks.
30. Agent Development Kit
● MadKit (http://www,madkit.net)
● MadKit is an open source modular and
scalable multiagent platform written in
Java and built upon the AGR
(Agent/Group/Role) organizational
model. MadKit agents play roles in
groups and thus create artificial
societies.
31. Agent Development Kit
● Mobile-C (http://www.mobilec.org/) is an IEEE FIPA
(Foundation for Intelligent Physical Agents) standard
compliant multi-agent platform for supporting C/C++
mobile agents in networked intelligent mechatronic
and embedded systems. Although it is a general-
purpose multi-agent platform, Mobile-C is specifically
designed for real-time and resource constrained
applications with interface to hardware. Mobile
agents are software components that are able to
move between different execution environments.
Mobile agents in a multi-agent system communicate
and work collaboratively with other agents to
achieve a global goal. It allows a mechatronic or
embedded system to adapt to a dynamically
changing environment.
32. Agent Development Kita
● KATO is PHP and Java-based agent
development kit intended towards the
development of personal assistant. It is
an open source project and available at
http://kato.sourceforge.net/
33. Agent Development Kita
● eXAT is an Erlang-based agent development kit. It is
intended to create MAS (Multi-Agent System).
According to the website, eXAT offering a multi-
agent programming platform composed of a set
modules able to provide the programmer with the
possibility of developing (with the same
programming language) agent behavior, by means
of definition of FSMs, agent intelligence, through the
provided expert system engine, and agent
collaboration.
● eXAT is available at
http://www.diit.unict.it/users/csanto/exat/index.html
34. Agent Development Kit
● Soar (http://sitemaker.umich.edu/soar/home) is a general
cognitive architecture for developing systems that exhibit
intelligent behavior. Soar is FOSS available under BSD
license. The intention to create Soar was to enable the
Soar architecture to:
– work on the full range of tasks expected of an
intelligent agent, from highly routine to extremely
difficult, open-ended problems
– represent and use appropriate forms of knowledge,
such as procedural, declarative, episodic, and
possibly iconic
– employ the full range of problem solving methods
– interact with the outside world, and
– learn about all aspects of the tasks and its
performance on them.
35. Agent Development Kit
● SPADE (Smart Python multi-Agent
Development Environment -
http://code.google.com/p/spade2/)
● An open source project which its aim is to
build a multiagent and organization
platform using Python, based on XMPP
technology.
36. Agent Development Kit
● Swarm (http://www.swarm.org/index.php/Main_Page)
● Swarm is a software package for multi-agent simulation of
complex systems, originally developed at the Santa Fe
Institute. Swarm is intended to be a useful tool for
researchers in the study of agent based models. Swarm
software comprises a set of code libraries which enable
simulations of agent based models to be written in the
Objective-C or Java computer languages. These libraries
will work on a very wide range of computer platforms. The
basic architecture of Swarm is the simulation of
collections of concurrently interacting agents: with this
architecture, we can implement a large variety of agent
based models. The Swarm software is available to the
general public under GNU licensing terms. Swarm is
experimental software, which means that it is complete
enough to be useful but will always be under
development.
37. Finish. Thank you for your kind
attention.
Question(s)? - I hope no.