slidesgo-optimizing-performance-an-in-depth-analysis-of-goal-based-agents-in-artificial-intelligence-20240808165324sg12.pptx

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1. Goal-Based Agents P resented By : Barun Kumar Singh University Roll No: (12500223125) B.TECH IT(B) 3r d Year BENGAL COLLEGE OF ENGINEERING & TECHNOLOGY Approved by AICTE, New Delhi and Affiliated to MAKAUT, Kolkata ISO 9001 : 2008 Certified Institute Department of Information Technology Presented to :- Mrs. Kamini Kanchan Subj: Artificial Intelligence Paper Code : PEC-IT501B

2. Introduction to Goal-Based Agents Goal-based agents are a crucial aspect of artificial intelligence. They operate by setting specific objectives and making decisions based on those goals. This presentation will explore their architecture, functionality, and the impact they have on optimizing performance in AI systems. Unlike simple reflex agents that act solely based on current perceptions, goal-based agents consider future consequences of their actions, ensuring that they align with the set objectives. In Generative AI, these agents have found a prominent role in generating content or solutions driven by specific end-goals.

3. Understanding Goal-Based Agents Goal-based agents utilize goal formulation to determine the best actions to achieve desired outcomes. They evaluate their environment and use planning and reasoning to navigate complex tasks efficiently. This slide delves into their fundamental principles and operational mechanics.

4. Key Characteristics The primary characteristics of goal-based agents include autonomy, reactivity, and proactiveness. They can adapt to changing environments while pursuing their objectives. Understanding these traits is essential for developing effective AI systems that can operate independently. Future-Oriented: They consider the implications of their actions on future states. Decision-making: Utilize decision-making algorithms to determine the best action to achieve their goal. Adaptive: Learn from the environment and improve over time to get closer to their goals. Goal Prioritization: Can prioritize between multiple goals based on the environment and context.

5. Architecture of Goal-Based Agents Goal-based agents typically consist of three main components: perception, reasoning, and action. These elements work together to process information and execute tasks. This slide outlines the architecture and interconnections between these component's.

6. P er f ormance Optimization Techniques Optimizing the performance of goal- based agents involves techniques such as heuristic search, dynamic programming, and machine learning. These methods enhance decision- making capabilities and allow agents to learn from experiences, improving their efficiency over time.

7. Applications Content Generation: Goal-based agents can be used in applications like content creation tools, where the agent aims to produce content that resonates with a target audience. Game Design: In video games, these agents can act as non-player characters with specific objectives, enhancing gameplay complexity and realism. Automated Design & Prototyping: Goal-based agents in Gen AI can design products or prototypes based on particular predefined objectives, considering factors like materials, cost, and desired performance. Personalized Marketing Intelligent Assistants Financial Trading

8. Challenges Faced Despite their advantages, goal-based agents face challenges such as environmental unpredictability , resource limitations , and goal conflicts . Addressing these issues is critical for enhancing their effectiveness and reliability in real- world applications.

9. Future Trends The future of goal-based agents lies in advancements in deep learning, reinforcement learning, and multi- agent systems. These trends promise to enhance their capabilities, making them more adept at handling complex tasks and dynamic environments.

10. Case Study: Robotics In robotics, goal-based agents are programmed to achieve specific tasks, such as navigation and object manipulation. This case study examines how these agents optimize performance in real-world scenarios, enhancing efficiency and effectiveness.

11. Evaluating Performance Evaluating the performance of goal-based agents involves metrics such as success rate, efficiency, and adaptability. Understanding these metrics is essential for assessing the effectiveness of AI systems and guiding future improvements.

12. In conclusion, goal-based agents play a pivotal role in optimizing performance within artificial intelligence. Their ability to set and pursue goals makes them essential in various applications. Continued research and development will further enhance their capabilities and impact. Conclusion

13. https://www.conted.ox.ac.uk/courses/samples/introduction-to-artificial-intelligence-online/index.html https://www.geeksforgeeks.org/agents-artificial-intelligence/ Refernces

14. Thank You