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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

A Phenomenologically Justifiable Simulation of Mental Modeling

verfasst von : Mark Wernsdorfer

Erschienen in: Artificial General Intelligence

Verlag: Springer International Publishing

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Abstract

Real-world agents need to learn how to react to their environment. To achieve this, it is crucial that they have a model of this environment that is adapted during interaction and although important aspects may be hidden. This paper presents a new type of model for partially observable environments that enables an agent to represent hidden states but can still be generated and queried in realtime. Agents can use such a model to predict the outcomes of their actions and to infer action policies. These policies turn out to be better than the optimal policy in a partially observable Markov decision process as it can be inferred, for example, by Q- or Sarsa-learning. The structure and generation of these models are motivated both by phenomenological considerations from semiotics and the philosophy of mind. The performance of these models is compared to a baseline of Markov models for prediction and interaction in partially observable environments.

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Vorheriges Kapitel Perception from an AGI Perspective
Nächstes Kapitel A Time-Critical Simulation of Language Comprehension
Fußnoten
1
In the last one, the authors explicitly state that “[t]he agent rarely observes the exact same frame from a previous episode” which makes the environment according to a state based conception practically be fully observable.
 
2
Comparisons with \(n \ge 2 \) yield similar results.
 
3
As a consequence, the performance of the baseline approach is lower than in experiments with memory reset.
 
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Metadaten
Titel
A Phenomenologically Justifiable Simulation of Mental Modeling
verfasst von
Mark Wernsdorfer
Copyright-Jahr
2018
Buch
Artificial General Intelligence

Print ISBN: 978-3-319-97675-4

Electronic ISBN: 978-3-319-97676-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-97676-1_26