Nenok - a Software Architecture for Generic Inference

Computing inference from a given knowledgebase is one of the key competences of computer science. Therefore, numerous formalisms and specialized inference routines have been introduced and implemented for this task. Typical examples are Bayesian networks, constraint systems or different kinds of logic. It is known today that these formalisms can be unified under a common algebraic roof called valuation algebra. Based on this system, generic inference algorithms for the processing of arbitrary valuation algebras can be defined. Researchers benefit from this high level of abstraction to address open problems independently of the underlying formalism. It is therefore all the more astonishing that this theory did not find its way into concrete software projects. Indeed, all modern programming languages for example provide generic sorting procedures, but generic inference algorithms are still mythical creatures. NENOK breaks a new ground and offers an extensive library of generic inference tools based on the valuation algebra framework. All methods are implemented as distributed algorithms that process local and remote knowledgebases in a transparent manner. Besides its main purpose as software library, NENOK also provides a sophisticated graphical user interface to inspect the inference process and the involved graphical structures. This can be used for educational purposes but also as a fast prototyping architecture for inference formalisms.

[1]  Steffen L. Lauritzen,et al.  Bayesian updating in causal probabilistic networks by local computations , 1990 .

[2]  Rina Dechter,et al.  Bucket Elimination: A Unifying Framework for Reasoning , 1999, Artif. Intell..

[3]  Mihalis Yannakakis,et al.  The Complexity of Multiterminal Cuts , 1994, SIAM J. Comput..

[4]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[5]  Prakash P. Shenoy,et al.  Binary join trees for computing marginals in the Shenoy-Shafer architecture , 1997, Int. J. Approx. Reason..

[6]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[7]  László A. Székely,et al.  On weighted multiway cuts in trees , 1994, Math. Program..

[8]  Jürg Kohlas.  Information algebras - generic structures for inference , 2003, Discrete mathematics and theoretical computer science.

[9]  Prakash P. Shenoy,et al.  Valuation-based systems: a framework for managing uncertainty in expert systems , 1992 .

[10]  Prakash P. Shenoy,et al.  Axioms for probability and belief-function proagation , 1990, UAI.

[11]  Eugene Charniak,et al.  Bayesian Networks without Tears , 1991, AI Mag..

[12]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[13]  Jan Newmarch,et al.  Foundations of Jini 2 Programming , 2006 .

[14]  Jürg Kohlas,et al.  Local Computation in Covering Join Trees Part #2 , 2006 .

[15]  Derek G. Corneil,et al.  Complexity of finding embeddings in a k -tree , 1987 .

[16]  Nic Wilson,et al.  Semiring induced valuation algebras: Exact and approximate local computation algorithms , 2008, Artif. Intell..

[17]  Ralph E. Johnson,et al.  Design Patterns: Abstraction and Reuse of Object-Oriented Design , 1993, ECOOP.

[18]  S. Aji.  Graphical Models and Iterative Decoding , 2000 .

[19]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[20]  Norbert Lehmann,et al.  Argumentation systems and belief functions , 2001 .

[21]  Marc Pouly,et al.  A generic Architecture for local Computation , 2022 .