Model-Based Reflex Agents
Model-based reflex agents enhance simple reflex agents by incorporating internal representations of the environment. These models allow agents to predict the outcomes of their actions and make more informed decisions. By maintaining internal states reflecting unobserved aspects of the environment and utilizing past perceptions, these agents develop a comprehensive understanding of the world. This approach equips them to effectively navigate complex environments, adapt to changing conditions, and handle partial observability.
Example: A self-driving system not only responds to present road conditions but also takes into account its knowledge of traffic rules, road maps, and past experiences to navigate safely.
Characteristics Model-Based Reflex Agents
- Adaptive: Maintains an internal model of the environment to anticipate future states and make informed decisions.
- Contextual Understanding: Considers both current input and historical data to determine appropriate actions, allowing for more nuanced decision-making.
- Computational Overhead: Requires resources to build, update, and utilize the internal model, leading to increased computational complexity.
- Improved Performance: Can handle more complex tasks and environments compared to simple reflex agents, thanks to its ability to incorporate past experiences.
Schematic Diagram of a Model-Based Reflex Agents
Types of Agents in AI
Types of Agents in AI, agents are the entities that perceive their environment and take actions to achieve specific goals. These agents exhibit diverse behaviours and capabilities, ranging from simple reactive responses to sophisticated decision-making. This article explores the different types of AI agents designed for specific problem-solving situations and approaches.
Table of Content
- 1. Simple Reflex Agent
- 2. Model-Based Reflex Agents
- 3. Goal-Based Agents
- 4. Utility-Based Agents
- 5. Learning Agents
- 6. Rational Agents
- 7. Reflex Agents with State
- 8. Learning Agents with a Model
- 9. Hierarchical Agents
- 10. Multi-agent systems