sumption Justification. Thus the architecture needs to call add dependencies to **support** -
set at most n times for any subtask consisting of n **assertions, and the worst case cost of**
updating the support set in a level remains O(n**). This reduction in complexity potentially makes Dynamic Hierarchical Justification a more efficient solution than Assumption**
Justification, especially as the number of local assertions **increases.**
Additionally, the two technical problems outlined for Assumption Justification do not impact DHJ. DHJ never needs to restore a previous assumption. When a dependency changes, the architecture retracts the entire level. Thus, **DHJ can immediately delete** replaced assumptions from memory. DHJ collects all dependencies for assumptions, so there is no need to switch from one justification to another. In Figure 8 (b), dependencies A, B, C, D, and E **are all added to the support set. These simplifications can make the**
support set overly specific but reduce the memory and computation overhead required by Dynamic Hierarchical Justification.

DHJ retractions will sometimes be followed by the regeneration **of the subtask and**
the re-assertion of reasoning that was retracted. For example, if the enemy plane fled as described in the TacAir-Soar scenario, DHJ would retract the entire level associated with the counting subtask. The count would then need to be re-started from the beginning.

Section 3.4.3 examines potential problems introduced by interruption and regeneration.

The cost incurred through the regeneration of previously-derived assertions is the primary drawback of Dynamic Hierarchical Justification.

## 3.4 Implications Of Dynamic Hierarchical Justification

Dynamic Hierarchical Justification solves the specific problem of maintaining reasoning consistency in a hierarchy, guaranteeing consistency and utilizing an efficient algorithm. The heuristic DHJ employs assumes that assumptions are so closely associated with their subtasks that retracting subtasks is nearly equivalent to retracting individual assumptions.

This section explores the implications of this heuristic, focusing on task decompositions, the impact on the agent's ability to use persistent assumptions, and the feasibility of interrupting an agent (with a subtask retraction) in the midst of reasoning.

## 3.4.1 The Influence Of The Task Decomposition

An agent's reasoning can be viewed as knowledge search (Newell, 1990). From this perspective, the inconsistency problem is failure to backtrack in knowledge search. The world changes, leading to changes in the agent hierarchy. The agent must retract some of the knowledge it has previously asserted, so it should backtrack to a knowledge state consistent with the world state.3 **Each solution can be described in terms of the way its achieves (or**
avoids) backtracking in the knowledge search. For instance, KBAC leads to knowledge-based backtracking, in which the KBAC knowledge tells the agent how to correct its assumptions given the current situation.