Name
Abstract | ||
|---|---|---|
Choosing an effective representation is fundamental to the ability of the representation's user to exploit it for the intended purpose. The major contribution of this paper is to provide a novel, flexible framework, rep2rep, that can be used by AI systems to recommend effective representations. What makes an effective representation is determined by whether it expresses the necessary information, supports the execution of tasks, and reflects the user's cognitive abilities. In general, there is no single `most effective' representation for every problem and every user, which makes it difficult to choose one from the plethora of possible representations. To address this, rep2rep includes: a domain-independent language for describing representations, algorithms that compute measures of informational suitability and overall cognitive cost, and uses these measures to recommend representations. We demonstrate the application of rep2rep in the probability domain. Importantly, our framework provides the foundations for personalised interaction with AI systems in the context of representation choice. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICTAI50040.2020.00185 | 2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) |
DocType | ISSN | Citations |
Conference | 1082-3409 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Raggi | 1 | 0 | 1.69 |
| Gem Stapleton | 2 | 482 | 56.25 |
| Aaron Stockdill | 3 | 0 | 1.69 |
| Mateja Jamnik | 4 | 158 | 30.79 |
| Grecia Garcia Garcia | 5 | 7 | 3.64 |
| Peter C H Cheng | 6 | 153 | 22.93 |