Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A note on the inevitability of maximum entropy
International Journal of Approximate Reasoning
Characterizing the principle of minimum cross-entropy within a conditional-logical framework
Artificial Intelligence
Conditional logic and the principle of entropy
Artificial Intelligence
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coherent knowledge processing at maximum entropy by spirit
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Recall and Reasoning-an information theoretical model of cognitive processes
Information Sciences: an International Journal
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XSPIRIT is a professional expert system-shell for knowledge acquisition, inference and response using conditional logic and probability. Composed conditionals on propositional variables with finite domain are the communication tool between the user and the knowledge base, making the process of acquisition, inference and query comfortable and intelligible. XSPIRIT allows partial rather than complete information about the knowledge domain and supplements missing parts by the principle of information fidelity. By virtue of evident temporary information, knowledge undergoes a well-defined adaptation process, respecting this principle again. The construction and transformation of probability distributions as developed here, allow acquired knowledge, remaining uncertainty and strength of inference to be measured in the information units [bit]. XSPIRIT allows large-scale applications with hundreds of composed conditionals and umpteen variables.