agent in e-lecyber security / الامن الالكترونيarning8-6-2021new.pptx

cyber security / الامن الالكتروني

1. Intelligent Agents in E-learning

2. Intelligent Agent user/ environment output/ sensors effectors input/ An intelligent agent is a system that : perceives its environment (which may be the physical world, a user via a graphical user interface, a collection of other agents , the Internet, or other complex environment), via sensors and acts rationally upon that environment with its effectors to realize a set of goals or tasks for which it was designed . reasons to interpret perceptions, draw inferences, solve problems, and determine actions. What is an intelligent agent?

3. Agent's environment:

4. Behavior Intelligent : -Perceiving one’s environment. -Learning and understanding from experience. - Knowledge - Communicating - Acting in complex environments - Reasoning to solve problems and discover hidden knowledge. - Thinking , Creativity, ingenuity.

5. Diagram of an agent

6. Ontology Rules/Cases/… Problem Solving Engine Intelligent Agent Output/ Sensors Effectors Input/ Knowledge Base User/ Environment Basic agent architecture:

7. There are two basic components of an agent: the knowledge base and the problem solving engine. The knowledge base contains data structures that represent the application domain. It includes representations of objects and their relations (the object ontology), but also representations of laws, actions, rules, cases or elementary problem solving methods. The problem solving engine implements a problem solving method that manipulates the data structures in the knowledge base to reason about the input problem, to solve it, and to determine the actions to perform next. That is, there is a clear separation between knowledge (which is contained into the knowledge base) and control (represented by the problem solving engine). The knowledge possessed by the agent and its reasoning processes should be understandable to humans. The agent should have the ability to give explanations of its behavior, what decisions it is making and why.

8. Structure of Intelligent Agents -The program runs on some kind of architecture To design an agent program, need to understand Percepts Actions Goals Environment

9. Agent function & program Agent’s behavior is mathematically described by: Agent function A function mapping any given percept sequence to an action Practically it is described by An agent program The real implementation

10. Intelligent agents are systems which can perform tasks requiring knowledge and heuristic methods. Intelligent agents are helpful, enabling us to do our tasks better. Intelligent agents are necessary to cope with the increasing complexity of the information society.

11. Agent types: Simple reflex agents Goal-based agents Reflex agents with state/model Utility-based agents All these can be turned into learning agents

12. E-Learning With the advancement of computer, multimedia and network technologies, alternatives to traditional classroom learning have been developed. E-Learning is one such alternative where students can access course-related materials via online computer systems. e-Learning is emerging as the way forward for lifelong learning. It refers to the use of Computers and Communication Technologies (CCT) in education.

13. There are three learning theories: -the cognitive theory -the constructivism theory -the traditional classroom face-to-face learning

14. Intelligent agents in e-learning -Many e-learning environments fail to offer good support compared with traditional learning environments. - Smartization of environments can fix this. -Smart environments can adjust changes with regard to user’s personal needs. - Smartization is achieved by using intelligent software agent technology. -Intelligent software agents are systems that make decisions autonomously and without user interaction. Intelligent agents would help user communicate with a computer program more efficiently.

15. Agents should support three types of services: -helping students perform innovative activities. -stimulating social behavior among students. -offering educators clear objective information about students' performance.

16. various agent applications in e-learning systems: Agent as Expert Experts exhibit mastery or extensive knowledge and perform better than the average within a domain. Agent as Motivator The Motivator suggests his own ideas, verbally encourages and stimulates the learners. Agent as Mentor An ideal human instructor provides guidance for the learner to bridge the gap between the current and desired skill levels.

17. The ability to improve its competence and performance. An agent is improving its competence if it learns to solve a broader class of problems, and to make fewer mistakes in problem solving. An agent is improving its performance if it learns to solve more efficiently (for instance, by using less time or space resources) the problems from its area of competence. Ability to learn

18. An agent should be able to communicate with its users or other agents. The communication language should be as natural to the human users as possible. Ideally, it should be free natural language. The problem of natural language understanding and generation is very difficult due to the ambiguity of words and sentences, the paraphrases, ellipses and references which are used in human communication. Ability to communicate

19. In order to solve "real-world" problems, an intelligent agent needs a huge amount of domain knowledge in its memory (knowledge base). the agent needs to have a lot of knowledge about the user, including the goals the user might want to achieve. Use of huge amounts of knowledge

20. A knowledge engineer attempts to understand how a subject matter expert reasons and solves problems and then encodes the acquired expertise into the agent's knowledge base. The expert analyzes the solutions generated by the agent (and often the knowledge base itself) to identify errors, and the knowledge engineer corrects the knowledge base. Knowledge Engineer Knowledge Base Problem Solving Engine Intelligent Agent Programming Dialog Results Subject Matter Expert How are agents built: Manual knowledge acquisition

21. A heuristic is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits the search for solutions in large problem spaces. Heuristics do not guarantee optimal solutions. In fact they do not guarantee any solution at all. A useful heuristic is one that offers solutions which are good enough most of the time. Intelligent agents generally attack problems for which no algorithm is known or feasible, problems that require heuristic methods. Use of heuristics

22. Refences: M.Asma,and O.Nadem ” Using Intelligent Agents in e-Learning”, Article  in  International Journal on Information · October 2013 A.hiba ,” Agent Applications In E-Learning Systems And Current Development And Challenges Of Adaptive E-Learning Systems”, June 2019 F.zhre,and R.Hamidreza “Study of application of intelligent agents in e-learning systems”, Bulletin de la Société Royale des Sciences de Liège, Vol. 86, special edition , 2017, p. 398 – 405 http://www.ai.sri.com/~or:eilly/aima3ejava/aima3ejavademos.html