3 research outputs found
Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering
We introduce multi-goal multi agent path finding (MAPF) which
generalizes the standard discrete multi-agent path finding (MAPF) problem.
While the task in MAPF is to navigate agents in an undirected graph from their
starting vertices to one individual goal vertex per agent, MAPF assigns
each agent multiple goal vertices and the task is to visit each of them at
least once. Solving MAPF not only requires finding collision free paths
for individual agents but also determining the order of visiting agent's goal
vertices so that common objectives like the sum-of-costs are optimized. We
suggest two novel algorithms using different paradigms to address MAPF:
a heuristic search-based search algorithm called Hamiltonian-CBS (HCBS) and a
compilation-based algorithm built using the SMT paradigm, called
SMT-Hamiltonian-CBS (SMT-HCBS). Experimental comparison suggests limitations of
compilation-based approach
Variable and value elimination in binary constraint satisfaction via forbidden patterns
Variable or value elimination in a constraint satisfaction problem (CSP) can
be used in preprocessing or during search to reduce search space size. A
variable elimination rule (value elimination rule) allows the polynomial-time
identification of certain variables (domain elements) whose elimination,
without the introduction of extra compensatory constraints, does not affect the
satisfiability of an instance. We show that there are essentially just four
variable elimination rules and three value elimination rules defined by
forbidding generic sub-instances, known as irreducible existential patterns, in
arc-consistent CSP instances. One of the variable elimination rules is the
already-known Broken Triangle Property, whereas the other three are novel. The
three value elimination rules can all be seen as strict generalisations of
neighbourhood substitution.Comment: A full version of an IJCAI'13 paper to appear in Journal of Computer
and System Sciences (JCSS