CSE 315 Presentation Automatic vehical.pptx

Networking and Internet data provide decision support opportunities, but face challenges like disorganization and inaccuracies. "Intelligent Software Agents" proposed to assist by gathering and managing information, enhancing decision-making.

1. Submitted To,,, Fahimul Islam Lecturer, Department of CSE , GUB. Smart agent and its effect on artificial intelligence : a review study. Friday, June 14, 2024 1

2. Pankaj Mahanta ID : 213902002 01 Meet Our Members We make a final decision by speaking with each work group members

3. Abstract. Introduction to AI. Human Intelligence & AI. Human Intelligence & AI. Introduction to Smart Agent. Chapter 1 Agent Variants. Intelligent Software Agent Architecture . Classification of Intelligent Software Agents. Chapter 2 The Agent Model. The Communication Model. Future areas of application. Swarm Intelligence. Conclusion. Chapter 3 Outline Friday, June 14, 2024 3

4. Slide 1 Slide 2 Slide 3 Slide 4 Networking and Internet data provide decision support opportunities, but face challenges like disorganization and inaccuracies. "Intelligent Software Agents" proposed to assist by gathering and managing information, enhancing decision-making. Abstract Friday, June 14, 2024 4

5. Slide 1 Slide 2 Slide 3 Slide 4 Introduction To AI AI history began mid-20th century, term coined by John McCarthy in 1956 at Dartmouth Conference. Early groundwork by pioneers like Alan Turing. After setbacks in '80s "AI winter," resurgence in 21st century with neural networks and machine learning. Breakthroughs in natural language processing, computer vision, and robotics. Today, AI revolutionizes healthcare, finance, transportation, and entertainment. Friday, June 14, 2024 5

6. Slide 1 Slide 2 Slide 3 Slide 4 What means Artificial Intelligence? How does it work? What means Human Intelligence? Human Intelligence & AI Friday, June 14, 2024 6

7. Slide 1 Slide 2 Slide 3 Slide 4 Human Intelligence vs AI Learning Adaptability Creativity Contextual Understand Social Interaction Emotional Intelligence Friday, June 14, 2024 7

8. Slide 1 Slide 2 Slide 3 Slide 4 Introduction to smart agent Intelligent Software Agents (ISAs) use AI to achieve client goals, mimicking human intelligence. ISAs excel in task delegation, establish trust with clients, and possess advanced functionality like data analysis and interaction with human media. Their adaptability ensures continual learning and performance improvement, making them resilient to obsolescence. Friday, June 14, 2024 8

9. Slide 1 Slide 2 Slide 3 Slide 4 Chapter 2 Table Of Contents Agent Variants Intelligent Software Agent Architecture Classification of Intelligent Software Agent... Classification of Intelligent Software Agent 10 11 12 13 TOPIC Friday, June 14, 2024 9

10. Slide 1 Slide 2 Slide 3 Slide 4 05 02 03 04 Mobile Agents Multiple Agents Distributed Agents Collaborative Agents 01 Social Agents Agent Variants Friday, June 14, 2024 10

11. Slide 1 Slide 2 Slide 3 Slide 4 Intelligent Software Agent Architecture In an agent-based distributed database system, mediator agents, supported by wrapper, matchmaker, and ontology agents, facilitate intelligent interoperability among diverse sources while decentralizing control. Wrapper agents provide access, extract content, and perform data conversion. Mediators translate ontology, execute queries, compose responses, and return results to users. Friday, June 14, 2024 11

12. Slide 1 Slide 2 Slide 3 Slide 4 Classification of Intelligent Software Agent… Agents can be classified according to the specific properties, or attributes, they exhibit. These include the following : 03 01 02 Agent mobility determines network traversal capacity, dividing between static and mobile agents, occasionally involving task cloning. Mobility Primary attributes are crucial; secondary attributes, less so. The primary ones encompass three key elements. Primary Attributes Agents can function autonomously, though human guidance may be invaluable at times, highlighting independence. Autonomy Friday, June 14, 2024 12

13. Slide 1 Slide 2 Slide 3 Slide 4 05 04 06 In MAS, agents possess inherent capability to exchange high-level information, crucial for collaboration. Co-operation Agents may possess secondary attributes like pro-active versatility, social abilities, and mental attitudes such as BDI's. Secondary Attributes Agents enhance performance through interaction with their environment. Collaborative, interface, and smart agents embody this concept. Learning Classification of Intelligent Software Agent Friday, June 14, 2024 13

14. Slide 1 Slide 2 Slide 3 Slide 4 The Agent Model Social Model Mental Model World Model Friday, June 14, 2024 14

15. Slide 1 Slide 2 Slide 3 Slide 4 Situation Beliefs Perception Goals Options Operational Primitives Intentions Action Friday, June 14, 2024 15

16. Slide 1 Slide 2 Slide 3 Slide 4 The Communication Model Input Processing Output Friday, June 14, 2024 16

17. Slide 1 Slide 2 Slide 3 Slide 4 Application & Future areas of application To date, ISAs have been successfully employed in multiple application endeavors Friday, June 14, 2024 17

18. Slide 1 Slide 2 Slide 3 Slide 4 Swarm Intelligence Decentralized Control Based on the collective behavior of elements in decentralized and self-organized systems . Emergent Behavior Complex behaviors can emerge from the interactions of simple individual agents . Friday, June 14, 2024 18

19. Conclusion capable of finding, organizing, and analyzing information on behalf of their owners . Agent Design Communication Agents must effectively perceive, process, and act on their environment. Applications Agents and swarm intelligence have a wide range of real-world applications.

20. Friday, June 14, 2024 20 Reference Russell, S., & Norvig, P. (2002 Artificial intelligence: a modern approach Asaad, R. R., Ahmad, H. B., & Ali, R. I. (2020) A Review: Big Data Technologies with Hadoop Distributed Filesystem and Implementing M/R. Academic Journal of Nawroz University, 9(1), 25-33. Mitchell, R. S., Michalski, J. G., & Carbonell , T. M. (2013). An artificial intelligence approach. Berlin: Springer. Almufti , S. M., & Shaban , A. A. (2018). U-Turning Ant Colony Algorithm for Solving Symmetric Traveling Salesman Problem. Academic Journal of Nawroz University, 7(4), 45-49. Asaad, R. R. (2014). An Investigation of the Neuronal Dynamics Under Noisy Rate Functions (Doctoral dissertation, Eastern Mediterranean University (EMU)- Doğu Akdeniz Üniversitesi (DAÜ)). Marashdih , A. W., Zaaba , Z. F., & Almufti , S. M. (2018). he Problems and Challenges of Infeasible Paths in Static Analysis. International Journal of Engineering & Technology, 7(4.19), 412-417. Reference

21. Thank You Friday, June 14, 2024 21 Keep Learning, Keep Grinding