6,800 research outputs found
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
A Methodology for Information Flow Experiments
Information flow analysis has largely ignored the setting where the analyst
has neither control over nor a complete model of the analyzed system. We
formalize such limited information flow analyses and study an instance of it:
detecting the usage of data by websites. We prove that these problems are ones
of causal inference. Leveraging this connection, we push beyond traditional
information flow analysis to provide a systematic methodology based on
experimental science and statistical analysis. Our methodology allows us to
systematize prior works in the area viewing them as instances of a general
approach. Our systematic study leads to practical advice for improving work on
detecting data usage, a previously unformalized area. We illustrate these
concepts with a series of experiments collecting data on the use of information
by websites, which we statistically analyze
Causes and Explanations: A Structural-Model Approach, Part I: Causes
We propose a new definition of actual cause, using structural equations to
model counterfactuals. We show that the definition yields a plausible and
elegant account of causation that handles well examples which have caused
problems for other definitions and resolves major difficulties in the
traditional account.Comment: Part II of the paper (on Explanation) is also on the arxiv.
Previously the two parts were submitted as one paper. To appear in the
British Journal for the Philosophy of Scienc
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