Pavlos Peppas , Mary-Anne Williams , Grigoris Antoniou
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Despite the great theoretical advancements in the area of Belief Revision, there has been limited success in terms of implementations. One of the hurdles in implementing revision operators is that their specification (let alone their computation), requires substantial resources. On the other hand, implementing a specific revision operator, like Dalal's operator, would be of limited use. In this paper we generalise Dalal's construction, defining a whole family of concrete revision operators, called Parametrised Difference revision operators or PD operators for short. This family is wide enough to cover a wide range of different applications, and at the same time it is easy to represent. In addition to its semantic definition, we characterise the family of PD operators axiomatically (including a characterisation specifically for Dalal's operator), we prove its' compliance with Parikh's relevance-sensitive postulate (P), we study its computational complexity, and discuss its benefits for belief revision implementations.
期刊介绍:
The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.