4 research outputs found
Improving Heuristics Through Relaxed Search - An Analysis of TP4 and HSP*a in the 2004 Planning Competition
The hm admissible heuristics for (sequential and temporal) regression
planning are defined by a parameterized relaxation of the optimal cost function
in the regression search space, where the parameter m offers a trade-off
between the accuracy and computational cost of theheuristic. Existing methods
for computing the hm heuristic require time exponential in m, limiting them to
small values (m andlt= 2). The hm heuristic can also be viewed as the optimal
cost function in a relaxation of the search space: this paper presents relaxed
search, a method for computing this function partially by searching in the
relaxed space. The relaxed search method, because it computes hm only
partially, is computationally cheaper and therefore usable for higher values of
m. The (complete) hm heuristic is combined with partial hm heuristics, for m =
3,..., computed by relaxed search, resulting in a more accurate heuristic.
This use of the relaxed search method to improve on the hm heuristic is
evaluated by comparing two optimal temporal planners: TP4, which does not use
it, and HSP*a, which uses it but is otherwise identical to TP4. The comparison
is made on the domains used in the 2004 International Planning Competition, in
which both planners participated. Relaxed search is found to be cost effective
in some of these domains, but not all. Analysis reveals a characterization of
the domains in which relaxed search can be expected to be cost effective, in
terms of two measures on the original and relaxed search spaces. In the domains
where relaxed search is cost effective, expanding small states is
computationally cheaper than expanding large states and small states tend to
have small successor states
Using explanation structures to speed up local-search-based planning
Master'sMASTER OF ENGINEERIN
Short Term Unit Commitment as a Planning Problem
‘Unit Commitment’, setting online schedules for generating units in a power system to ensure supply meets demand, is integral to the secure, efficient, and economic daily operation of a power system. Conflicting desires for security of supply at minimum cost complicate this. Sustained research has produced methodologies within a guaranteed bound of optimality, given sufficient computing time.
Regulatory requirements to reduce emissions in modern power systems have necessitated increased renewable generation, whose output cannot be directly controlled, increasing complex uncertainties. Traditional methods are thus less efficient, generating more costly schedules or requiring impractical increases in solution time.
Meta-Heuristic approaches are studied to identify why this large body of work has had little industrial impact despite continued academic interest over many years. A discussion of lessons learned is given, and should be of interest to researchers presenting new Unit Commitment approaches, such as a Planning implementation.
Automated Planning is a sub-field of Artificial Intelligence, where a timestamped sequence of predefined actions manipulating a system towards a goal configuration is sought. This differs from previous Unit Commitment formulations found in the literature. There are fewer times when a unit’s online status switches, representing a Planning action, than free variables in a traditional formulation. Efficient reasoning about these actions could reduce solution time, enabling Planning to tackle Unit Commitment problems with high levels of renewable generation.
Existing Planning formulations for Unit Commitment have not been found. A successful formulation enumerating open challenges would constitute a good benchmark problem for the field. Thus, two models are presented. The first demonstrates the approach’s strength in temporal reasoning over numeric optimisation. The second balances this but current algorithms cannot handle it. Extensions to an existing algorithm are proposed alongside a discussion of immediate challenges and possible solutions. This is intended to form a base from which a successful methodology can be developed