Agents in Artificial intelligence
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The document discusses different types of intelligent agents based on their architecture and programs. It describes five classes of agents: 1) Simple Reflex agents which react solely based on current percepts; 2) Model-based agents which use an internal model of the world; 3) Goal-based agents which take actions to reduce distance to a goal; 4) Utility-based agents which choose actions to maximize utility; and 5) Learning agents which are able to learn from experiences. The document also outlines different forms of learning like rote learning, learning from instruction, and reinforcement learning.
Agents in Artificial intelligence
- 1. The Structure of Agents • There are two parts in agents 1. Architecture: Hardware with sensors and actuators. 2. Program: Convert percepts into actions. Agents are classified into 5 classes, based on their agent programs. Lets us discuss them
- 2. 1. Simple Reflex (SR) Agents • No internal state, act only on the basis of current percept. • Operate correctly only in fully observable environment. • Simple functions are built based on sensory input ie. condition action rule. • A boundary following robot is SR agent.
- 3. Simple Reflex (SR) Agents Limitations • Very limited intelligence. • Usually too big to generate and store. • Not flexible, need to update the rules if any change occurs in environment.
- 4. 2. Model Based Agents • Find a rule whose condition matches the current situation. • Handle partially observable environments by using model. • The agent has internal state, adjusted by each percept and that depends on the percept history. • Current state stored inside the agents, describing the part of the world which cannot be seen. • Updating the state requires information about : • how the world evolves in-dependently from the agent, and • how the agent actions affects the world.
- 5. Model Based Reflex Agents
- 6. 3. Goal Based Agents • Extension of model based agents. • Take decision based on how far they are currently from their goal. • Every action is intended to reduce its distance from the goal. • Agent choose a way among multiple possibilities, selecting the one which reaches a goal state. • Searching and planning. • Agent needs some sort of looking into future.
- 8. 4. Utility Based Agents • Main focus on utility not goal. • Used when there are multiple possible alternatives • Actions based on preference ( Utility). • Utility describes how happy the agent is. • Agent chooses the action that maximize utility.
- 9. 5. Learning Agent • Learn from past experiences • It start with knowledge then able to act and adapt automatically. • It has 4 components 1. Learning element: It is responsible for making improvements by learning from the environment 2. Critic: Learning element takes feedback from critic which describes how well the agent is doing with respect to a fixed performance standard.
- 10. Learning Agent 3. Performance element: Responsible for selecting external action, based on percept and feedback from learning element . 4. Problem Generator: Suggest actions that will lead to new and informative experiences.
- 11. Different forms of learning • Rote learning or memorization. • Least amount of inferencing. • Knowledge is copied in knowledge base. • Learning through instructions • Learning by analogy • Development of new concepts through already known similar concepts • Learning by induction • Conclusion drawn based on large number of examples.
- 12. Different forms of learning • Learning by deduction • Irrefutable form of reasoning. • Concepts drawn always already correct, if given facts are correct. • Learning based on feedback • Supervised • Unsupervised • Reinforcement learning