Automated decision making is one of the important problems of
Artificial Intelligence (AI).
Planning and scheduling are two sub-fields of AI that research
automated decision making. The
main focus of planning is on general representations of actions,
causal reasoning among actions
and domain-independent solving strategies. Scheduling generally
optimizes problems with
complex temporal and resource constraints that have simpler
causal relations between actions.
However, there are problems that have both planning
characteristics (causal constraints) and
scheduling characteristics (temporal and resource constraints),
and have strong interactions
between these constraints. An integrated approach is needed to
solve this class of problems
efficiently.
The main contribution of this thesis is an integrated
constraint-based planning and scheduling
approach that can model and solve problems that have both
planning and scheduling characteristics.
In our representation problems are described using a multi-valued
state variable
planning language with explicit representation of different types
of resources, and a new action
model where each action is represented by a set of transitions.
This action-transition model
makes the representation of actions with delayed effects, effects
with different durations, and
the representation of complex temporal and resource constraints
like time-windows, deadline
goals, sequence-dependent setup times, etc simpler.
Constraint-based techniques have been successfully applied to
solve scheduling problems.
Therefore, to solve a combined planning/scheduling problem we
compile it into a CSP. This
compilation is bounded by the number of action occurrences. The
constraint model is based
on the notion of “support” for each type of transition. The
constraint model can be viewed
as a system of CSPs, one for each state variable and resource,
that are synchronized by a
simple temporal network for action start times. Central to our
constraint model is the explicit
representation and maintenance of the precedence constraints
between transitions on the same
state variable or resource.
We propose a branching scheme for solving the CSP based on
establishing supports for
transitions, which imply precedence constraints. Furthermore, we
propose new propagation
and inference techniques that infer precedence relations from
temporal and mutex constraints,
and infer tighter temporal bounds from the precedence
constraints. The distinguishing feature
of these inference and propagation techniques is that they not
only consider the transitions and
actions that are included in the plan but can also consider
actions and transitions that are not
yet included in or excluded from the plan.
We conclude the thesis with a modeling case study of a complex
satellite problem domain
to demonstrate the effectiveness of our representation. This
problem domain has action choices
that are tightly coupled with temporal and resource constraints.
We show that most of the
complexities of this problem can be expressed in our
representation in a simple and intuitive
way
