Abstract
| Language | English |
|---|---|
| Pages | 123 - 137 |
| Number of pages | 15 |
| Journal | International Journal of Data Mining, Modelling and Management |
| Volume | 4 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2012 |
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Keywords
- clustering
- automated planning
- complexity reduction
- planning domains
- artificial intelligence
- data modelling
- data mining
Cite this
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Abstraction through clustering : complexity reduction in automated planning domains. / Dicken, L.; Gregory, P.; Levine, J.
In: International Journal of Data Mining, Modelling and Management, Vol. 4, No. 2, 2012, p. 123 - 137.Research output: Contribution to journal › Article
TY - JOUR
T1 - Abstraction through clustering
T2 - International Journal of Data Mining, Modelling and Management
AU - Dicken, L.
AU - Gregory, P.
AU - Levine, J.
PY - 2012
Y1 - 2012
N2 - Automated planning is a very active area of research within artificial intelligence. Broadly this discipline deals with the methods by which an agent can independently determine the sequence of actions required to successfully achieve a set of objectives. In this paper, we will present work outlining a new approach to planning based on clustering techniques, in order to group states of the world together and use the fundamental structure of the world to lift out more abstract representations. We will show that this approach can limit the combinatorial explosion of a typical planning problem in a way that is much more intuitive and reusable than has previously been possible, and outline ways that this approach can be developed further.
AB - Automated planning is a very active area of research within artificial intelligence. Broadly this discipline deals with the methods by which an agent can independently determine the sequence of actions required to successfully achieve a set of objectives. In this paper, we will present work outlining a new approach to planning based on clustering techniques, in order to group states of the world together and use the fundamental structure of the world to lift out more abstract representations. We will show that this approach can limit the combinatorial explosion of a typical planning problem in a way that is much more intuitive and reusable than has previously been possible, and outline ways that this approach can be developed further.
KW - clustering
KW - automated planning
KW - complexity reduction
KW - planning domains
KW - artificial intelligence
KW - data modelling
KW - data mining
UR - http://www.scopus.com/inward/record.url?scp=84870280055&partnerID=8YFLogxK
U2 - 10.1504/IJDMMM.2012.046806
DO - 10.1504/IJDMMM.2012.046806
M3 - Article
VL - 4
SP - 123
EP - 137
JO - International Journal of Data Mining, Modelling and Management
JF - International Journal of Data Mining, Modelling and Management
SN - 1759-1163
IS - 2
ER -