4,822 research outputs found
Learning Symbolic Models of Stochastic Domains
In this article, we work towards the goal of developing agents that can learn
to act in complex worlds. We develop a probabilistic, relational planning rule
representation that compactly models noisy, nondeterministic action effects,
and show how such rules can be effectively learned. Through experiments in
simple planning domains and a 3D simulated blocks world with realistic physics,
we demonstrate that this learning algorithm allows agents to effectively model
world dynamics
Learning relational dynamics of stochastic domains for planning
Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.Peer ReviewedPostprint (author's final draft
Learning relational dynamics of stochastic domains for planning
Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.Peer ReviewedPostprint (author's final draft
KR: An Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture that combines the complementary
strengths of declarative programming and probabilistic graphical models to
enable robots to represent, reason with, and learn from, qualitative and
quantitative descriptions of uncertainty and knowledge. An action language is
used for the low-level (LL) and high-level (HL) system descriptions in the
architecture, and the definition of recorded histories in the HL is expanded to
allow prioritized defaults. For any given goal, tentative plans created in the
HL using default knowledge and commonsense reasoning are implemented in the LL
using probabilistic algorithms, with the corresponding observations used to
update the HL history. Tight coupling between the two levels enables automatic
selection of relevant variables and generation of suitable action policies in
the LL for each HL action, and supports reasoning with violation of defaults,
noisy observations and unreliable actions in large and complex domains. The
architecture is evaluated in simulation and on physical robots transporting
objects in indoor domains; the benefit on robots is a reduction in task
execution time of 39% compared with a purely probabilistic, but still
hierarchical, approach.Comment: The paper appears in the Proceedings of the 15th International
Workshop on Non-Monotonic Reasoning (NMR 2014
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