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Figure 1: Plans are composted of scenes, with a script marking the actual execution path.

A "plan" is an internal data-structure of the Planner. A plan consists of a tree with one root task which is decomposed to a sequence of sub-tasks in the first level of the tree. When executing a plan, a Script is created with the actual states of the real world during plan execution. The reverse operation of Plan Recognition constructs a plan from a given Script.

A plan may be converted into a 2D Scene using a "pen on paper" representation, typically pruning task decompositions after the 1st level. This 2D Scene is then accessible for self-reference and meta-reasoning by the higher reasoning faculties, may become optimized or combined with other plans and then be converted again into an executable plan.


TinyCog 0.0.1 implements deterministic plans (currently with a single outcomes per action). TinyCog 0.0.3 will introduce plans with alternative outcomes per task when elaborating on the Hunter Domain.