agivation

Agivation is a theoretical framework used in artificial intelligence and cognitive science to describe an integrated process by which an agent simultaneously plans its trajectory through an environment and selects actions that achieve goals. The concept emphasizes coupling of goal-directed decision making with spatial navigation, so that movement decisions are informed by long-term objectives and environmental constraints.

The term appears in several early 21st-century discussions as a portmanteau of action-orientation and navigation, sometimes described as a synthesis of action planning and pathfinding. It is not yet widely standardized, and different authors use the term with varying emphasis on planning, perception, or control.

In models of agivation, control is typically provided by a hierarchical or hybrid architecture. A high-level policy specifies goals or subgoals; a lower-level navigator computes routes or motor commands. A coordinator integrates signals, resolves conflicts, and updates plans when sensing reveals new obstacles or changing rewards. Techniques from reinforcement learning, model predictive control, and probabilistic planning are common, with emphasis on re-planning under uncertainty and real-time adaptation.

Applications include autonomous vehicles, robotics, game AI, and simulation. Evaluations focus on robustness, efficiency, and the alignment of navigation with objectives. Challenges include computational complexity, safety guarantees, and interpretability of the integrated decision process.