ture (GN) that records substantial information associated with each goal node (for example, goal, associated constraints, mode, selected expansion/plan,
plan expectation, associated discrepancies).
Goal refinement is only one extension of plan
refinement, which equates multiple planning algorithms in plan-space and state-space planning. Other extensions incorporate different forms of planning
and clarify issues in the Modal Truth Criterion
(Kambhampati and Nau 1994). More recent formalisms (for example, Angelic Hierarchical Plans
[Marthi, Russell, and Wolfe 2008] and Hierarchical
Goal Networks [Shivashankar, Alford, and Aha 2017])
can also be viewed as leveraging plan refinement.
Employing constraints in plan refinement allows a
natural extension to the many integrated planning
and scheduling systems that use constraints for temporal and resource reasoning.
Our Goal Lifecycle resembles the one proposed by
Harland et al. (2014) for BDI agents, which they provide operational semantics for and demonstrate on a
Mars rover scenario. Winikoff, Dastani, and van
Riemsdijk (2010) have linked linear temporal logic
(LTL) to the expression of goals. As described later, we
have as well, though our work with Goal Reasoning
with Information Measures (GRIM) focuses on agent
teams rather than single agents.
In summary, the Goal Lifecycle provides a formal
structure for goal refinement, such that the GR agent
can deliberate on and adapt its goals in response to
dynamic and unpredictable events. As described
next, our GR agents employ variants of GDA or more
comprehensive Goal Lifecycle models.
To date, our GR applications have focused on controlling autonomous unmanned vehicles, either simulated or hardware. This section summarizes three
such applications: the first employs the GDA model,
the second uses a substantial modification of it, and
the third instantiates the Goal Lifecycle.
This section briefly summarizes initial studies on
using GR to control an unmanned underwater vehicle (UUV). Details on the implementation and results
can be found in Wilson et al. (2018).
UUVs can perform several important missions (for
example, surveillance, mine countermeasures, plume
source localization, hull inspection), which motivate
a high demand for robust UUV control methods.
These vehicles must operate in mission environments that, unlike others (for example, ground, air,
Figure 4. A Depiction of a Goal Lifecycle Model of Goal Reasoning.
• Discrepancy Detector
• Explanation Generator
• Goal Lifecycle Strategies
UI d,e M GN s