AGM Theory and Artificial Intelligence

  • Raúl CarnotaEmail author
  • Ricardo Rodríguez
Part of the Logic, Epistemology, and the Unity of Science book series (LEUS, volume 21)


A very influential work on belief change is based on the seminal paper “On the Logic of Theory Change: Partial Meet Contractions and Revision Functions”. In this paper Alchourron, Gärdenfors, and Makinson investigate the properties that such a method should have in order to be intuitively appealing. The result was a set of postulates (named AGM postulates after the initials of the authors) that every belief change operator should satisfy. The “AGM Theory” had a major influence in most subsequent works on belief change. In special, the constructive approach given there was adopted in Artificial Intelligence (AI) as the (almost) universal model for the specification of the updates of Knowledge Bases as it usually involves a new belief that may be inconsistent with the old ones. From that moment, the references to the original paper within the field of AI have increased drastically. In this chapter we intend to explain why AGM receive such swift acceptance from the AI community. We will show that the AGM theory came out at a critical time for AI. In particular, the question of how to actualize the knowledge bases in the face of inputs that are possible inconsistent with the previous corpus, was an unresolved, problematic issue. This is why, when AGM appears, it combines with various attempts under development that synchronize perfectly with them, as is the case with the pioneering work we will be discussing in this chapter. The high level of abstraction of the new approach was what a lot of researchers in AI were seeking.

This chapter also contains a description of the genesis of AGM theory, a historical analysis of the circumstances in which the theory was introduced to AI, and a qualitative and quantitative evaluation of the impact of the AGM theory in AI research.


Knowledge Level Belief Revision Belief Base Belief Change Conditional Sentence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would not be able even to attempt to reconstruct the history of the introduction of the AGM Theory in Artificial Intelligence without invaluable contributions of many of the principle actors of those years’ long, extensive and effervescent discussions. All of them approached our inquiry with enthusiasm and made true efforts to honor us with their emotive memories.

Therefore we want to thank Cristina Bicchieri, Horacio Arló Costa, Alex Borgida, Mukesh Dalal, Norman Foo, Peter Gärdenfors, Joseph Halpern, Gilbert Harman, David Israel, Hirofumi Katsumo, Hector Levesque, Bernhard Nebel, Hans Rott, Ken Satoh, Karl Schlechta for accepting to respond our questions and being our partners this time.

We are very grateful to our friend Eduardo Fermé for his sharp observations that helped us to revise the original Spanish draft of our work.

Also, we are indebted to our colleague and friend José Alvarez who helped us to translate to English our first draft. In addition, during this process, he offered us a lot of helpful and interesting discussions on different parts of this chapter.

We own a very special gratitude to Gladys Palau, Eugenio Bulygin, David Makinson and Antonio Martino who committed themselves in a very personal way to the reconstruction of the history, patiently accepting our repeated inquiries and generously sharing their reminiscences of Carlos Alchourrón.

Thanks all of them to accompany us to honor the memory of Carlos Alchourrón whose disciples and friends we consider ourselves to be.


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© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Universidad Nacional de Tres de FebreroBuenos AiresArgentina
  2. 2.Departamento de Computación de la Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresBuenos AiresArgentina

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