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Compare Model Based agent, goal-based agent and utility-based agent with diagrams.

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Aspect Model-Based Agent Goal-Based Agent Utility-Based Agent
Knowledge Representation Maintains an explicit model of the world, including the current state and how it evolves. Focuses on goals, representing the desired states or outcomes. Incorporates utility functions, representing preferences over outcomes.
Decision Making Process Uses the model to simulate actions, predict outcomes, and choose actions that lead to desired states. Selects actions based on their contribution to achieving goals, often employing search algorithms. Selects actions based on their expected utility, maximizing overall satisfaction.
Adaptability Adapts well to changes in the environment, as it can update its model dynamically. Adapts to changes in goals, adjusting its plan or approach based on evolving objectives. Adapts to changes in preferences, considering new information and adjusting actions accordingly.
Computational Complexity Can be computationally intensive, especially if the world model is complex or uncertain. Search algorithms may be computationally expensive, depending on the size and complexity of the goal space. Evaluation of utility functions may require computation, especially in complex decision scenarios.
Handling Uncertainty Can explicitly represent uncertainty in the model, allowing for probabilistic reasoning. May handle uncertainty in goals through probabilistic goal achievement models. Can incorporate uncertainty in utility functions, accounting for risk preferences.
Flexibility High flexibility due to the ability to model a wide range of scenarios and adapt to changes. Moderate flexibility as it can adapt to changing goals, but the scope is defined by the goal space. Moderate flexibility as it can adjust to changing preferences, but the utility function constrains the decision space.
Example Applications Robotics, autonomous systems, where a detailed model of the environment is crucial. Planning systems, where achieving predefined goals is the primary objective. Economic decision-making, where actions are selected to maximize overall satisfaction.
Risk Management Can explicitly model and analyze risks, enabling risk-aware decision-making. May incorporate risk considerations in goal achievement strategies. May incorporate risk preferences in the utility function, reflecting aversion or tolerance to risk.
Scalability May face scalability challenges as the complexity of the model increases. Scalability depends on the complexity of the goal space but can become challenging for extensive goals. Scalability challenges may arise if the utility function involves a large number of factors or complex computations.
Real-Time Decision Making May struggle in real-time scenarios, especially if model updates are time-consuming. Real-time decision-making can be feasible, but the efficiency depends on the complexity of the goal space.

May handle real-time decision-making, but the computational load depends on the utility function complexity.





Model-Based Reflex Agents

Model-Based Reflex Agents


Goal-Based Agents

Goal-Based Agents


Utility-Based Agents

Utility-Based Agents



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