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Contingent Planning via an Action-Selection Mechanism
Planning under partial observability is the general case
of automatic planning, where only part of the domain information is available at run-time.
This formulation of planning is the one that reflects more the real
non-deterministic behaviour of a dynamic domain; however, it is also the most complex
and demanding planning task.
Safe Assumption-Based Planning
A large number of relevant planning problems require the ability to
deal with partially observable, non-deterministic domain models.
Using assumptions (i.e. expected or nominal domain behaviours) to restrict
the search is a well-known approach to alleviate the high complexity of
planning problems, focusing the search on expected domain behaviours.
To effectively deal with complex planning problems for partial observable
and non-deterministic domains, we define
safe assumption-based solution plans
as those solution plans built under assumption that guarantee that
any successful execution is observationally distinguishable from any unsuccessful execution.
Memory bounds may limit the ability of a reasoner to make inferences
and therefore affect the reasoner's usefulness. We
propose a framework to automatically verify the reasoning capabilities
of propositional memory-bounded reasoners which have a sequential
Robotic Navigation & Localisation
Robotic navigation problem, given their reliance on
sensors and actuators that have a limited precision,
involves the challenge of developing efficient robot localization
processes, on the one hand, and integrating them with goal-driven
behaviours on the other hand. Current approaches for this
integration consider localization behaviours and goal-driven
behaviours as two separate interleaved processes. This can lead to
suboptimal or even failing robot behaviours, where decision choices
made by one process negatively interfere with those made by the
other, and where serendipitous situations created by one process
are not exploited by the other.
We propose a new idea for integrating localization and goal-driven behaviours that considers requirements for both types of behaviours as an integrated specification for a planning process. Sensory-activated and goal-driven plans are generated by applying a planning method that handles partially observable nondeterminisitc domains. This is obtained by a framework that integrates, within a behavioural robotic architecture, a planner providing strong solution plans for nondeterministic and partially observable domains (i.e. plans that achieve the goal whatever the initial uncertainty about the domain is).