01728naa a2200253 a 450000100080000000500110000800800410001902000220006002400410008210000180012324501040014126000090024530000160025449000590027052007990032965000280112865000240115665000140118065300170119465300420121165300290125365300320128277301600131419855792020-01-08       2014    bl uuuu     u00u1 u    #d  a978-1-4666-4936-17 a10.4018/978-1-4666-4936-1.ch0182DOI1 aCASTRO, A. de  aA Shannon-like solution for the fundamental equation of information science.h[electronic resource]  c2014  ap. 516-524.  a(Advances in computacional intelligence and robotics).  aIn a seminal paper published in the early 1980s titled ?Information Technology and the Science of Information,? Bertram C. Brookes theorized that a Shannon-Hartley's logarithmic-like measure could be applied to both information and recipient knowledge structure in order to satisfy his ?Fundamental Equation of Information Science.? To date, this idea has remained almost forgotten, but, in what follows, the authors introduce a novel quantitative approach that shows that a Shannon-Hartley's log-like model can represent a feasible solution for the cognitive process of retention of information described by Brookes. They also show that if, and only if, the amount of information approaches 1 bit, the ?Fundamental Equation? can be considered an equality in stricto sensu, as Brookes required.  aArtificial intelligence  aInformation science  aKnowledge  aConhecimento  aEquação da ciência da informação  aInteligência artificial  aInteligência computacional  tIn: TRIPATHY, B. K.; ACHARIVA, D. P. Global trends in intelligent computing research and development. Hershey: Information Science Reference, 2014. ch. 18.