In the late eighties inference in KL-ONE
was shown to be undecidable.
Since then the emphasis in research
has been on developing and investigating
systems that are computationally well behaved, i.e. are tractable or
at least decidable.
As a result many commonly used description logics (also known as
terminological logics or KL-ONE-based knowledge
have restricted expressiveness and are in their current form not
suitable for natural language applications.
This is evident, for example, from Schmidt  who links knowledge
representation with a relational approach
to natural language semantics.
For encoding knowledge formulated in a very limited fragment of
English we already need the full expressive power of
role constructs which have been eliminated in many languages.
We share the view of Doyle and Patil  who argue for
expressiveness as opposed to computational efficiency.
Our experience with users interested in user modelling and
natural language simulations can be summarized as follows:
In our sample application we model the dialogue between two agents: a car salesperson
and a customer.
Agents have the following properties:
- Users want expressiveness.
- They want representation languages with more
basic features than just concepts, roles and individuals (i.e. A-Box
elements) and operations on these.
- And, they want special inference tools.
In our approach to agent modelling and natural language processing
we use an extension of the well-known description language .
Our system MOTEL serves on one hand as a knowledge base for the natural
language front-end, and on the other hand, it provides powerful
logical representation and reasoning components.
As our approach is logic based we
hope that this enhances the overall capabilities of the natural language processing (NLP) system.
- They communicate in natural language.
- They actively pursue complex goals, which may be conflicting.
- They have the means of analyzing (some of) the pragmatic content of what
is being said, i.e., they have a deeper understanding
of `belief', `intension' or `argument'.
In the following sections we describe MOTEL and
the different extensions we are working on.
Up: The MOTEL Knowledge Representation System
Previous: The MOTEL Knowledge Representation System
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