Learning alternative ways of performing a task
| Autor: | Mj. Ramírez-Quintana, Carlos Monserrat, José Hernández-Orallo, David Nieves, Cèsar Ferri |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science Generalization Process mining 02 engineering and technology Machine learning computer.software_genre Task learning Domain (software engineering) Task (project management) Activity recognition 020901 industrial engineering & automation Resource (project management) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Iterative and incremental development Measure (data warehouse) business.industry General Engineering Inductive learning Identifying strategies Computer Science Applications 020201 artificial intelligence & image processing Artificial intelligence business computer LENGUAJES Y SISTEMAS INFORMATICOS |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname RiuNet: Repositorio Institucional de la Universitat Politécnica de Valéncia Universitat Politècnica de València (UPV) |
| Popis: | [EN] A common way of learning to perform a task is to observe how it is carried out by experts. However, it is well known that for most tasks there is no unique way to perform them. This is especially noticeable the more complex the task is because factors such as the skill or the know-how of the expert may well affect the way she solves the task. In addition, learning from experts also suffers of having a small set of training examples generally coming from several experts (since experts are usually a limited and ex- pensive resource), being all of them positive examples (i.e. examples that represent successful executions of the task). Traditional machine learning techniques are not useful in such scenarios, as they require extensive training data. Starting from very few executions of the task presented as activity sequences, we introduce a novel inductive approach for learning multiple models, with each one representing an alter- native strategy of performing a task. By an iterative process based on generalisation and specialisation, we learn the underlying patterns that capture the different styles of performing a task exhibited by the examples. We illustrate our approach on two common activity recognition tasks: a surgical skills training task and a cooking domain. We evaluate the inferred models with respect to two metrics that measure how well the models represent the examples and capture the different forms of executing a task showed by the examples. We compare our results with the traditional process mining approach and show that a small set of meaningful examples is enough to obtain patterns that capture the different strategies that are followed to solve the tasks. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN2014-61716-EXP (SUPERVASION) and RTI2018-094403-B-C32, and by Generalitat Valenciana under grant PROMETEO/2019/098. David Nieves is also supported by the Spanish MINECO under FPI grant (BES-2016-078863). |
| Databáze: | OpenAIRE |
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