Planning on English AI Terms Dictionary

Means–ends analysis

Sat, 18 Jul 2026 10:06:58 +0000

Definition

Means-ends analysis is a cognitive strategy used in artificial intelligence and psychology to solve complex problems. It involves comparing the current state of a problem to the desired goal state, identifying the differences between them, and then selecting operators or actions that reduce those specific differences. If a direct action is not possible, the method breaks the problem down into smaller subgoals. This recursive decomposition allows agents to navigate large state spaces efficiently by focusing on immediate obstacles to progress toward the final objective.

GOLOG

Sat, 18 Jul 2026 09:58:48 +0000

Definition

GOLOG is a logic-based programming language used primarily in artificial intelligence for planning and acting in dynamic environments. Built upon Reiter’s situation calculus, it allows developers to specify complex sequences of actions and high-level goals that are then compiled into executable low-level commands. It is particularly useful in robotics and automated systems where precise reasoning about action effects, preconditions, and frame problems is required to ensure correct behavior in changing contexts.

And–or tree

Sat, 18 Jul 2026 09:45:36 +0000

Definition

An And–or tree is a representation used in problem-solving and planning, particularly in AI search algorithms. ‘Or’ nodes represent choices between different actions, while ‘And’ nodes indicate that all subsequent sub-nodes must be satisfied to achieve a goal. This structure helps decompose complex problems into manageable subproblems, facilitating efficient search strategies like AO* for finding optimal solutions in non-deterministic environments.

Summary

A hierarchical graph structure used in search algorithms where nodes represent states and edges represent actions leading to subgoals.

Agentic

Sat, 18 Jul 2026 09:39:58 +0000

Definition

The term ‘agentic’ describes AI agents that operate with a high degree of autonomy. Unlike passive models that simply predict text or classify data, agentic systems can break down complex objectives into sub-tasks, use tools, interact with environments, and iterate on their actions to solve problems. This paradigm shifts AI from being a reactive tool to a proactive collaborator. These systems often employ memory, planning mechanisms, and reflection loops to improve performance over time, enabling them to handle dynamic and unstructured real-world scenarios effectively.

long-horizon

Sat, 18 Jul 2026 09:38:47 +0000

Definition

Long-horizon problems involve sequences of actions where the impact of early decisions manifests only after many steps. This is common in robotics, planning, and multi-step reasoning tasks. The challenge lies in credit assignment—determining which past actions contributed to current outcomes—and maintaining consistency over time. Algorithms must balance immediate gains with long-term objectives, often requiring sophisticated memory mechanisms or hierarchical planning strategies to succeed.

Summary

Refers to tasks requiring decision-making over extended timeframes with delayed rewards or consequences.

decision-making

Sat, 18 Jul 2026 09:38:20 +0000

Definition

In artificial intelligence, decision-making refers to the algorithmic process where a system evaluates potential actions against specific criteria or objectives to select the optimal outcome. This involves analyzing state observations, predicting consequences, and applying utility functions or reward structures to maximize long-term goals. It is fundamental to autonomous agents, robotics, and strategic planning systems that operate in dynamic environments without constant human intervention.

Summary

The cognitive process of selecting a course of action from multiple alternatives based on available information.