Linear planning logic and linear logic graph planner: domain independent task planners based on linear logic
Author(s)
Advisor
Akman, VarolDate
2017-09Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
219
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329
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Abstract
Linear Logic is a non-monotonic logic, with semantics that enforce single-use
assumptions thereby allowing native and e cient encoding of domains with dynamic
state. Robotic task planning is an important example for such domains,
wherein both physical and informational components of a robot's state exhibit
non-monotonic properties. We introduce two novel and e cient theorem provers
for automated construction of proofs for an exponential multiplicative fragment
of linear logic to encode deterministic STRIPS planning problems in general.
The rst planner we introduce is Linear Planning Logic (LPL), which is based on
the backchaining principle commonly used for constructing logic programming
languages such as Prolog and Lolli, with a novel extension for LPL to handle
program formulae with non-atomic conclusions. We demonstrate an experimental
application of LPL in the context of a robotic task planner, implementing
visually guided autonomous navigation for the RHex hexapod robot. The second
planner we introduce is the Linear Logic Graph Planner (LinGraph), an
automated planner for deterministic, concurrent domains, formulated as a graphbased
theorem prover for a propositional fragment of intuitionistic linear logic.
The new graph-based theorem prover we introduce in this context substantially
improves planning performance by reducing proof permutations that are irrelevant
to planning problems particularly in the presence of large numbers of objects
and agents with identical properties (e.g. robots within a swarm, or parts in a
large factory). We illustrate LinGraph's application for planning the actions of
robots within a concurrent manufacturing domain and provide comparisons with
four existing automated planners, BlackBox, Symba-2, Metis and the Temporal
Fast Downward (TFD), covering a wide range of state-of-the-art automated
planning techniques and implementations that are well-known in the literature for
their performance on various of problem types and domains. We show that even though LinGraph does not rely on any heuristics, it still outperforms these systems
for concurrent domains with large numbers of identical objects and agents,
nding feasible plans that they cannot identify. These gains persist even when
existing methods on symmetry reduction and numerical
uents are used, with
LinGraph capable of handling problems with thousands of objects. Following
these results, we also formally show that plan construction with LinGraph is
equivalent to multiset rewriting systems, establishing a formal relation between
LinGraph and intuitionistic linear logic.