Parsing PCFG within a General Probabilistic Inference Framework
Arthi Murugesan, Nicholas L. Cassimatis
Available Online June 2009.
- https://doi.org/10.2991/agi.2009.46How to use a DOI?
- One of the aims of Artificial General Intelligence(AGI) is to use the same methods to reason over a large num- ber of problems spanning different domains. Therefore, advancing general tools that are used in a number of domains like language, vision and intention reading is a step toward AGI. Probabilistic Context Free Gram- mar (PCFG) is one such formalism used in many do- mains. However, many of these problems can be dealt with more effectively if relationships beyond those en- coded in PCFGs (category, order and parthood) can be included in inference. One obstacle to using more general inference approaches for PCFG parsing is that these approaches often require all state variables in a domain to be known in advance. However, since some PCFGs license infinite derivations, it is in general im- possible to know all state variables before inference. Here, we show how to express PCFGs in a new proba- bilistic framework that enables inference over unknown objects. This approach enables joint reasoning over both constraints encoded by a PCFG and other con- straints relevant to a problem. These constraints can be encoded in a first-order language that in addition to encoding causal conditional probabilities can also represent (potentially cyclic) boolean constraints.
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Cite this article
TY - CONF AU - Arthi Murugesan AU - Nicholas L. Cassimatis PY - 2009/06 DA - 2009/06 TI - Parsing PCFG within a General Probabilistic Inference Framework BT - Proceedings of the 2nd Conference on Artificiel General Intelligence (2009) PB - Atlantis Press SP - 218 EP - 223 SN - 1951-6851 UR - https://doi.org/10.2991/agi.2009.46 DO - https://doi.org/10.2991/agi.2009.46 ID - Murugesan2009/06 ER -