The concept of horizon in AI, especially in the context of reinforcement learning (RL), is crucial for determining how far into the future an agent should consider the consequences of its actions. A long horizon implies that the agent evaluates the potential long-term outcomes and rewards, which is essential for tasks where the final goal or significant rewards are far in the future. Conversely, a short horizon focuses on immediate or near-term consequences, making it suitable for environments where quick responses or immediate rewards are prioritized. The choice between a long and short horizon affects the agent's strategy, computational complexity, and ability to handle delayed rewards. Long horizons can lead to more optimal solutions but require more sophisticated algorithms and greater computational resources, whereas short horizons are simpler but might miss long-term benefits.
The concept of horizon in decision-making and planning has been part of AI since the early days of reinforcement learning in the 1980s. The distinction between long and short horizons became more prominent in the 1990s and 2000s as RL techniques were refined and applied to more complex problems.
Richard Sutton and Andrew Barto, through their foundational work in reinforcement learning, have significantly contributed to the understanding and development of horizon concepts. Their textbook "Reinforcement Learning: An Introduction" has been pivotal in disseminating these ideas. Additionally, researchers like Peter Dayan and Dmitry Bertsekas have also made substantial contributions to this field.
In AI, choosing the appropriate horizon length is essential for balancing computational efficiency and achieving optimal outcomes, impacting the design and success of intelligent systems across various applications.