# Situational Control

**Origin.** Dmitry Pospelov and the Soviet AI school, Institute of Control Sciences (IPU), Moscow, 1970s-1980s. Developed as a distinct approach from Western AI's focus on search and logic. Emphasized knowledge-intensive reasoning about structured wholes rather than state-space search.

**Mechanism.** The world is represented as structured situations rather than states in a state space. A situation is a configuration of objects, relations, and context that has meaning as a whole; the same objects in different relations constitute different situations. Control proceeds by recognizing the current situation type, retrieving the appropriate response schema, and instantiating it. Expertise is encoded as situation-action pairs, not as goal-means chains. The method is knowledge-intensive rather than search-intensive.

**Procedure.** Design and implement situational control systems: (1) **Enumerate situation types** — what distinct situations must the system handle? Each type is a recognizable configuration with its own appropriate response. Work with domain experts to elicit the situation vocabulary. (2) **Define recognition criteria** — for each situation type, what observable features identify it? What configuration of sensor data, context, and history signals this situation? Recognition criteria should be robust to noise. (3) **Define response schemas** — for each situation type, what actions are appropriate? In what sequence? With what parameters? What adaptations based on situation specifics? (4) **Implement matching** — when new observations arrive, match against situation types. Use pattern matching, classification, or rule-based recognition. (5) **Handle boundaries** — situations near type boundaries may match multiple types or none. Design default behaviors for ambiguous cases and escalation paths for unrecognized situations. (6) **Execute and adapt** — on match, instantiate and execute the response schema. Monitor outcomes to refine recognition criteria and responses.

**Applies to.** Expert systems. Diagnostic systems. Robotics. Operational procedures. Any domain where expertise consists of recognizing situations and applying appropriate responses — troubleshooting, medical diagnosis, process control.

**Limitations.** Brittle at situation boundaries: real situations are continuous, but the method discretizes them into types. Observations near boundaries produce unstable recognition. The enumeration of situation types is complete only for well-understood domains; novel situations fall through. The method encodes expertise but does not generate it — it cannot handle situations the designer did not anticipate. Knowledge acquisition from experts is labor-intensive.

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