Image Credentials: Image Title: Planning and Decision-Making Source: (Gemini) Date: September 2025. Attribution: Created using AI-generated imagery (Gemini), this does not depict a real-world scene.
By Open Chronicle
Planning and decision-making are central concepts in artificial intelligence (AI) that concern how intelligent agents choose actions to achieve goals or maximize preferences. An agent is defined as any system that perceives its environment and takes actions within it. A rational agent operates with goals or preferences and selects actions that are expected to bring about desired outcomes.
Goals and Preferences
In automated planning, the agent is given a specific goal, such as reaching a destination or assembling a sequence of tasks. By contrast, in automated decision-making, the agent has preferences: some states of the world are more desirable than others, and some are to be avoided.
Agents assign a numerical value, known as utility, to measure how much they prefer a given state. For each possible action, the agent calculates the expected utility—the weighted average of utilities over all possible outcomes, with weights corresponding to the probability of each outcome. The agent then selects the action with the highest expected utility.
Classical and Real-World Planning
In classical planning, the agent operates under the assumption that:
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It knows the exact state of the world, and
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The consequences of its actions are deterministic.
In real-world problems, however, uncertainty is common. The agent may not know precisely what situation it is in (the world may be partially observable), and the outcomes of its actions may be uncertain (the world is non-deterministic). In these contexts, agents must make probabilistic guesses, take actions, and then reassess the situation based on new information.
Uncertainty in Preferences
In some cases, an agent’s preferences may themselves be uncertain, especially when decisions involve other agents or human users. Preferences can be:
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Learned through methods such as inverse reinforcement learning.
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Refined by actively seeking additional information to reduce uncertainty.
Information value theory provides a framework for quantifying the usefulness of exploratory actions, weighing the cost of exploration against the expected improvement in decision quality.
Decision Frameworks
Markov Decision Processes
A Markov decision process (MDP) provides a mathematical framework for decision-making under uncertainty. It consists of:
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A transition model: probabilities that an action will change the environment from one state to another.
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A reward function: assigns utilities to states and costs to actions.
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A policy: a strategy that specifies which action to take in each state.
Policies can be calculated algorithmically (e.g., through value iteration), designed heuristically, or learned from experience.
Game Theory
Game theory describes rational behavior among multiple interacting agents. It is used in AI systems that must make strategic decisions in competitive, cooperative, or adversarial settings. Examples include negotiation, auctions, and multi-agent simulations.
Real-World Applications
Planning and decision-making frameworks are used in a wide range of AI applications:
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Autonomous vehicles: Self-driving cars like those developed by Waymo or Tesla use decision-making algorithms to navigate uncertain environments. They must plan safe routes, avoid obstacles, and make real-time trade-offs between speed, safety, and efficiency.
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Robotics: Household robots and industrial robots rely on planning to complete multi-step tasks. For example, a robot vacuum must decide the optimal path to clean a room, while a factory robot coordinates actions to assemble products efficiently.
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Healthcare decision support: AI-driven planning tools assist doctors in diagnosing conditions and recommending treatments. Systems must balance probabilities of success, patient-specific data, and risks associated with alternative medical decisions.
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Recommendation systems: Online platforms like Netflix, Amazon, and YouTube use decision-making algorithms to suggest products, videos, or content. These systems optimize recommendations by weighing user preferences and past behavior to maximize long-term engagement.
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Finance and trading: Automated trading agents use planning and game theory to predict market movements and decide on investment strategies in real time, often competing against other agents in highly dynamic environments.
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Military and defense applications: AI planning systems are used for logistics, drone navigation, and strategic simulations, where agents must reason about adversaries’ potential actions.
References
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Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
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Bellman, R. (1957). Dynamic Programming. Princeton University Press.
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Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley.
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Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
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Von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.
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Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). “Planning and Acting in Partially Observable Stochastic Domains.” Artificial Intelligence, 101(1–2), 99–134.
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Howard, R. A. (1966). “Information Value Theory.” IEEE Transactions on Systems Science and Cybernetics, 2(1), 22–26.
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