Knowledge Representation and Reasoning with Answer Set Programming

Knowledge Representation and Reasoning with Answer Set Programming

Read about the difference between declarative and imperative programming and learn from code examples (Answer Set Programming, Python and C).

Read about the difference between declarative and imperative programming and learn from code examples (Answer Set Programming, Python and C).

Introduction

The amount of computational problems seems to be unlimited in both industry and science. There is a huge demand for new insights from the vast amount of available data. To obtain this knowledge, dedicated people use all kinds of programming languages for designing and implementing algorithms.

However, many of the current real-world problems are complex (combinatorial) search problems with which we try to automate and optimize processes as well as support people in their decisions. This does not involve encoding of algorithms but encoding given knowledge. In other words, given knowledge about all the rules and constraints to be considered to what is counted as a solution. Instead of writing statements describing the control flow of a computation, declarative programming expresses its logic. What do we want to achieve, not statements about how we can achieve it.

This is called declarative programming.

Why declarative programming?

Using a promising declarative programming paradigm, namely Answer Set Programming (ASP, sometimes also Answer Set Prolog), offers unexpected advantages over the popular imperative programming approach, for example:

  • Short solutions (as measured by lines of code)
  • Transparency (including readability)
  • Reliability

and many more. Once you have broken the problem down into its smallest pieces and written down all the necessary knowledge, you will not only solve the computational challenge. A big advantage is that you have digitized the knowledge and can use it for further problems or make it available in other ways.

Experience and knowledge can be sustainably secured and protected against loss (e.g. through retirement or termination of employees).

Furthermore, it strengthens the cooperation between ‘business people’ and programmers. The integration of artificial intelligence is supported and acceptance at all levels will increase. This is a fundamental process that can only be achieved in cooperation with all departments of a company.

ai & machine learning knowledge programming reasoning

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