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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.04758 |
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| _version_ | 1866915050399203328 |
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| author | MacDermott, Matt Fox, James Belardinelli, Francesco Everitt, Tom |
| author_facet | MacDermott, Matt Fox, James Belardinelli, Francesco Everitt, Tom |
| contents | We define maximum entropy goal-directedness (MEG), a formal measure of goal-directedness in causal models and Markov decision processes, and give algorithms for computing it. Measuring goal-directedness is important, as it is a critical element of many concerns about harm from AI. It is also of philosophical interest, as goal-directedness is a key aspect of agency. MEG is based on an adaptation of the maximum causal entropy framework used in inverse reinforcement learning. It can measure goal-directedness with respect to a known utility function, a hypothesis class of utility functions, or a set of random variables. We prove that MEG satisfies several desiderata and demonstrate our algorithms with small-scale experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Measuring Goal-Directedness MacDermott, Matt Fox, James Belardinelli, Francesco Everitt, Tom Artificial Intelligence Machine Learning We define maximum entropy goal-directedness (MEG), a formal measure of goal-directedness in causal models and Markov decision processes, and give algorithms for computing it. Measuring goal-directedness is important, as it is a critical element of many concerns about harm from AI. It is also of philosophical interest, as goal-directedness is a key aspect of agency. MEG is based on an adaptation of the maximum causal entropy framework used in inverse reinforcement learning. It can measure goal-directedness with respect to a known utility function, a hypothesis class of utility functions, or a set of random variables. We prove that MEG satisfies several desiderata and demonstrate our algorithms with small-scale experiments. |
| title | Measuring Goal-Directedness |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.04758 |