A Phenomenologically Justifiable Simulation of Mental Modeling
| Autor: | Mark Wernsdorfer |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Philosophy of mind
Structure (mathematical logic) Computer science business.industry Partially observable Markov decision process Observable Machine learning computer.software_genre Markov model Action (philosophy) Reinforcement learning Artificial intelligence Baseline (configuration management) business computer |
| Zdroj: | Artificial General Intelligence ISBN: 9783319976754 AGI |
| DOI: | 10.1007/978-3-319-97676-1_26 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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