the GDA model to reason about sensing actions that
have associated costs and about the way in which different methods for generating expectations impact a
GDA model’s performance.
While GDA can model simple GR processes, it does
not explicitly model goal constraints, the relation of
goals to tasks for achieving them, or processes for suspending or revising goals whose plans are not executable. This limitation motivated the development
of a more comprehensive process model for GR. In
Roberts et al. (2014), we introduced such a model,
based on goal refinement, an extension of plan
refinement (Kambhampati, Knoblock, and Yang
1995) that models the progressive refinement of
goals through the addition of constraints. Goal
refinement can represent the context in which a goal
is pursued by a GR agent. Our goal refinement
process model is the Goal Lifecycle. Figure 3 displays
a simplified version of it. This model transitions a
goal node (that is, a pairing GN = (g, C) of goal g with
constraint set C) through increasingly detailed
modes (for example, formulated, selected) by applying constraint-refinement strategies that progress
goal nodes toward completion. The strategies include
formulate, select, expand, commit, and dispatch.
Formulate creates a new goal node and enters it into
the Goal Lifecycle by defining its initial constraints,
criteria, and prerequisites.
Select chooses which goal(s) to actively pursue; it
ensures that the goals’ prerequisites are met and that
the agent has the resources to pursue them.
Expand generates a set of expansions X (for example,
plans, decompositions of nonprimitive goals, or trajectories of primitive goals) to achieve a goal g in goal
node GN, and a set of expectations for each.
Commit picks an expansion x ∊ X to pursue from those
generated by expand.
Dispatch executes the committed expansion and
defines the criteria by which g can be evaluated during
The Goal Lifecycle also includes strategies for
reacting to events and changes during execution.
After being dispatched, a goal expansion is monitored and, if a discrepancy is detected, it can be evaluated. As a result, the GR agent may continue executing the expansion, it may drop GN (as either
completed or failed), or it may try to resolve the discrepancy through one of several strategies (for example, repair, defer) that transition GN to an earlier
mode before execution resumes. This approach supports goal adaptation, deferment, and even reformulation. The Goal Lifecycle captures decision points
during a goal’s activation, and can be represented as
a set of decide subprocesses (figure 4) where this life-cycle’s strategies subsume the decision processes
denoted in figure 2, and we introduce a data struc-
Figure 3. The Goal Lifecycle — A Goal Refinement Model of Goal Reasoning.
Formulated Selected Expanded Committed Dispatched Evaluated Finished