654,026 research outputs found
Learning Action Models: Qualitative Approach
In dynamic epistemic logic, actions are described using action models. In
this paper we introduce a framework for studying learnability of action models
from observations. We present first results concerning propositional action
models. First we check two basic learnability criteria: finite identifiability
(conclusively inferring the appropriate action model in finite time) and
identifiability in the limit (inconclusive convergence to the right action
model). We show that deterministic actions are finitely identifiable, while
non-deterministic actions require more learning power-they are identifiable in
the limit. We then move on to a particular learning method, which proceeds via
restriction of a space of events within a learning-specific action model. This
way of learning closely resembles the well-known update method from dynamic
epistemic logic. We introduce several different learning methods suited for
finite identifiability of particular types of deterministic actions.Comment: 18 pages, accepted for LORI-V: The Fifth International Conference on
Logic, Rationality and Interaction, October 28-31, 2015, National Taiwan
University, Taipei, Taiwa
A group-theoretic approach to formalizing bootstrapping problems
The bootstrapping problem consists in designing agents that learn a model of themselves and the world, and utilize it to achieve useful tasks. It is different from other learning problems as the agent starts with uninterpreted observations and commands, and with minimal prior information about the world. In this paper, we give a mathematical formalization of this aspect of the problem. We argue that the vague constraint of having "no prior information" can be recast as a precise algebraic condition on the agent: that its behavior is invariant to particular classes of nuisances on the world, which we show can be well represented by actions of groups (diffeomorphisms, permutations, linear transformations) on observations and commands. We then introduce the class of bilinear gradient dynamics sensors (BGDS) as a candidate for learning generic robotic sensorimotor cascades. We show how framing the problem as rejection of group nuisances allows a compact and modular analysis of typical preprocessing stages, such as learning the topology of the sensors. We demonstrate learning and using such models on real-world range-finder and camera data from publicly available datasets
Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model
Most human behaviors consist of multiple parts, steps, or subtasks. These
structures guide our action planning and execution, but when we observe others,
the latent structure of their actions is typically unobservable, and must be
inferred in order to learn new skills by demonstration, or to assist others in
completing their tasks. For example, an assistant who has learned the subgoal
structure of a colleague's task can more rapidly recognize and support their
actions as they unfold. Here we model how humans infer subgoals from
observations of complex action sequences using a nonparametric Bayesian model,
which assumes that observed actions are generated by approximately rational
planning over unknown subgoal sequences. We test this model with a behavioral
experiment in which humans observed different series of goal-directed actions,
and inferred both the number and composition of the subgoal sequences
associated with each goal. The Bayesian model predicts human subgoal inferences
with high accuracy, and significantly better than several alternative models
and straightforward heuristics. Motivated by this result, we simulate how
learning and inference of subgoals can improve performance in an artificial
user assistance task. The Bayesian model learns the correct subgoals from fewer
observations, and better assists users by more rapidly and accurately inferring
the goal of their actions than alternative approaches.Comment: Accepted at AAAI 1
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