Pretrained Embeddings as a Behavior Specification Mechanism
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866929744182771712 |
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| author | Kapoor, Parv Hammer, Abigail Kapoor, Ashish Leung, Karen Kang, Eunsuk |
| author_facet | Kapoor, Parv Hammer, Abigail Kapoor, Ashish Leung, Karen Kang, Eunsuk |
| contents | We propose an approach to formally specifying the behavioral properties of systems that rely on a perception model for interactions with the physical world. The key idea is to introduce embeddings -- mathematical representations of a real-world concept -- as a first-class construct in a specification language, where properties are expressed in terms of distances between a pair of ideal and observed embeddings. To realize this approach, we propose a new type of temporal logic called Embedding Temporal Logic (ETL), and describe how it can be used to express a wider range of properties about AI-enabled systems than previously possible. We demonstrate the applicability of ETL through a preliminary evaluation involving planning tasks in robots that are driven by foundation models; the results are promising, showing that embedding-based specifications can be used to steer a system towards desirable behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pretrained Embeddings as a Behavior Specification Mechanism Kapoor, Parv Hammer, Abigail Kapoor, Ashish Leung, Karen Kang, Eunsuk Artificial Intelligence Robotics Software Engineering We propose an approach to formally specifying the behavioral properties of systems that rely on a perception model for interactions with the physical world. The key idea is to introduce embeddings -- mathematical representations of a real-world concept -- as a first-class construct in a specification language, where properties are expressed in terms of distances between a pair of ideal and observed embeddings. To realize this approach, we propose a new type of temporal logic called Embedding Temporal Logic (ETL), and describe how it can be used to express a wider range of properties about AI-enabled systems than previously possible. We demonstrate the applicability of ETL through a preliminary evaluation involving planning tasks in robots that are driven by foundation models; the results are promising, showing that embedding-based specifications can be used to steer a system towards desirable behaviors. |
| title | Pretrained Embeddings as a Behavior Specification Mechanism |
| topic | Artificial Intelligence Robotics Software Engineering |
| url | https://arxiv.org/abs/2503.02012 |