Name
Abstract | ||
|---|---|---|
There are various activity recognition approaches that rely on manual definition of precondition-effect rules to describe human behaviour. These rules are later used to generate computational models of human behaviour that are able to reason about the user behaviour based on sensor observations. One problem with these approaches is that the manual rule definition is time consuming and error prone process. To address this problem, in this paper we propose an approach that learns the rules from textual instructions. In difference to existing approaches, it is able to learn the causal relations between the actions without initial training phase. Furthermore, it learns the domain ontology that is used for the model generalisation and specialisation. To evaluate the approach, a model describing cooking task was learned and later applied for explaining seven plans of actual human behaviour. It was then compared to a hand-crafted model describing the same problem. The results showed that the learned model was able to recognise the plans with higher overall probability compared to the hand-crafted model. It also learned a more complex domain ontology and was more general than the hand-crafted model. In general, the results showed that it is possible to learn models of human behaviour from textual instructions which are able to explain actual human behaviour. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005755604150422 | ICAART |
Field | DocType | Citations |
Ontology,Activity recognition,Computer science,Generalization,Causal relations,Computational model,Artificial intelligence,Learning models,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kristina Yordanova | 1 | 70 | 15.22 |
| Thomas Kirste | 2 | 117 | 25.44 |