Zhang, W.-R., Pandurangi, K.A., Peace, K.E., Zhang, Y. and Zhao, Z. (2011) Mental Squares—A Generic Bipolar Support Vector Machine for Psychiatric Disorder Classification, Diagnostic Analysis and Neurobiological Data Mining. International Journal on Data Mining and Bioinformatics, 5, 532-557.

has been cited by the following article:

• TITLE: Causality Is Logically Definable—Toward an Equilibrium-Based Computing Paradigm of Quantum Agent and Quantum Intelligence (QAQI) (Survey and Research)

  AUTHORS: Wen-Ran Zhang, Karl E. Peace

  KEYWORDS: Causality and Definability, Causal Logic, Causal Algebra, Quantum Agent, Quantum Intelligence, Quantum Non-Locality, Quantum Logic Gate, Energy-Information Conservation, Laws of Causation, CPT Symmetry, Mind-Body Unification, Growing and Aging, Quantum Biology, Quantum Data Mining

  JOURNAL NAME: Journal of Quantum Information Science, Vol.4 No.4, December 22, 2014

  ABSTRACT: A survey on agents, causality and intelligence is presented and an equilibrium-based computing paradigm of quantum agents and quantum intelligence (QAQI) is proposed. In the survey, Aristotle’s causality principle and its historical extensions by David Hume, Bertrand Russell, Lotfi Zadeh, Donald Rubin, Judea Pearl, Niels Bohr, Albert Einstein, David Bohm, and the causal set initiative are reviewed; bipolar dynamic logic (BDL) is introduced as a causal logic for bipolar inductive and deductive reasoning; bipolar quantum linear algebra (BQLA) is introdused as a causal algebra for quantum agent interaction and formation. Despite the widely held view that causality is undefinable with regularity, it is shown that equilibrium-based bipolar causality is logically definable using BDL and BQLA for causal inference in physical, social, biological, mental, and philosophical terms. This finding leads to the paradigm of QAQI where agents are modeled as quantum enssembles; intelligence is revealed as quantum intelligence. It is shown that the enssemble formation, mutation and interaction of agents can be described as direct or indirect results of quantum causality. Some fundamental laws of causation are presented for quantum agent entanglement and quantum intelligence. Applicability is illustrated; major challenges are identified in equilibriumbased causal inference and quantum data mining.