  Service robots need to reason to support people in daily life situations.
Reasoning is an expensive resource that should be used on demand whenever the
expectations of the robot do not match the situation of the world and the
execution of the task is broken down; in such scenarios the robot must perform
the common sense daily life inference cycle consisting on diagnosing what
happened, deciding what to do about it, and inducing and executing a plan,
recurring in such behavior until the service task can be resumed. Here we
examine two strategies to implement this cycle: (1) a pipe-line strategy
involving abduction, decision-making and planning, which we call deliberative
inference and (2) the use of the knowledge and preferences stored in the
robot's knowledge-base, which we call conceptual inference. The former involves
an explicit definition of a problem space that is explored through heuristic
search, and the latter is based on conceptual knowledge including the human
user preferences, and its representation requires a non-monotonic
knowledge-based system. We compare the strengths and limitations of both
approaches. We also describe a service robot conceptual model and architecture
capable of supporting the daily life inference cycle during the execution of a
robotics service task. The model is centered in the declarative specification
and interpretation of robot's communication and task structure. We also show
the implementation of this framework in the fully autonomous robot Golem-III.
The framework is illustrated with two demonstration scenarios.
