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**Objectives**

Leveraging PyPy flexibility implement language-integrated constraint
satisfaction algorithms and inference engine to allow logic
programming for Semantic Web applications developed at Logilab and DFKI.

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**Description of work**

**Task 1**

Using the flexible architecture provided by the PyPy interpreter, we
will first reimplement the current python-logic libraries available
from Logilab to better integrate with the language and gain important
execution speed-ups.

**Task 2**

This logic programming enabled Python interpreter will then be
used to further develop the projects related to Semantic Web
applications that are on-going at Logilab and DFKI.

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**Deliverables**

- D09.1 Implementation of constraint satisfaction engine and inference engine in PyPy

- D09.2 Assessment of benefits obtained from using PyPy over current tools to further develop Semantic Web projects at Logilab and DFKI

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**Milestones and Expected Result**

- Python interpreter exhibiting logic-programming features, such as
  inference and constraint satisfaction.

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