Representing First-Order Causal Theories by Logic Programs

Nonmonotonic causal logic, introduced by Norman McCain and Hudson Turner, became a basis for the semantics of several expressive action languages. McCain's embedding of definite propositional causal theories into logic programming paved the way to the use of answer set solvers for answering queries about actions described in such languages. In this paper we extend this embedding to nondefinite theories and to first-order causal logic.Comment: 29 pages. To appear in Theory and Practice of Logic Programming (TPLP); Theory and Practice of Logic Programming, May, 201

Biosemiosis and Causation: Defending Biosemiotics Through Rosen's Theoretical Biology, or, Integrating Biosemiotics and Anticipatory Systems Theory

The fracture in the emerging discipline of biosemiotics when the code biologist Marcello Barbieri claimed that Peircian biosemiotics is not genuine science raises anew the question: What is science? When it comes to radically new approaches in science, there is no simple answer to this question, because if successful, these new approaches change what is understood to be science. This is what Galileo, Darwin and Einstein did to science, and with quantum theory, opposing interpretations are not merely about what theory is right, but what is real science. Peirce's work, as he acknowledged, is really a continuation of efforts of Schelling to challenge the heritage of Newtonian science for the very good reason that the deep assumptions of Newtonian science had made sentient life, human consciousness and free will unintelligible, the condition for there being science. Pointing out the need for such a revolution in science has not succeeded as a defence of Peircian biosemiotics, however. In this paper, I will defend the scientific credentials of Peircian biosemiotics by relating it to the theoretical biology of the bio-mathematician, Robert Rosen. Rosen's relational biology, focusing on anticipatory systems and giving a place to final causes, should also be seen as a rigorous development of the Schellingian project to conceive nature in such a way that the emergence of sentient life, mind and science are intelligible. Rosen has made a very strong case for the characterization of his ideas as a real advance not only in science, but in how science should be understood, and I will argue that it is possible to provide a strong defence of Peircian biosemiotics as science through Rosen's defence of relational biology. In the process, I will show how biosemiotics can and should become a crucial component of anticipatory systems theory

Probabilistic theories with purification

We investigate general probabilistic theories in which every mixed state has a purification, unique up to reversible channels on the purifying system. We show that the purification principle is equivalent to the existence of a reversible realization of every physical process, namely that every physical process can be regarded as arising from a reversible interaction of the system with an environment, which is eventually discarded. From the purification principle we also construct an isomorphism between transformations and bipartite states that possesses all structural properties of the Choi-Jamiolkowski isomorphism in quantum mechanics. Such an isomorphism allows one to prove most of the basic features of quantum mechanics, like e.g. existence of pure bipartite states giving perfect correlations in independent experiments, no information without disturbance, no joint discrimination of all pure states, no cloning, teleportation, no programming, no bit commitment, complementarity between correctable channels and deletion channels, characterization of entanglement-breaking channels as measure-and-prepare channels, and others, without resorting to the mathematical framework of Hilbert spaces.Comment: Differing from the journal version, this version includes a table of contents and makes extensive use of boldface type to highlight the contents of the main theorems. It includes a self-contained introduction to the framework of general probabilistic theories and a discussion about the role of causality and local discriminabilit

Immanent Powers versus Causal Powers (Propensities, Latencies and Dispositions) in Quantum Mechanics

In this paper we compare two different notions of 'power', both of which attempt to provide a realist understanding of quantum mechanics grounded on the potential mode of existence. For this propose we will begin by introducing two different notions of potentiality present already within Aristotelian metaphysics, namely, irrational potentiality and rational potentiality. After discussing the role played by potentiality within classical and quantum mechanics, we will address the notion of causal power which is directly related to irrational potentiality and has been adopted by many interpretations of QM. We will then present the notion of immanent power which relates to rational potentiality and argue that this new concept presents important advantages regarding the possibilities it provides for understanding in a novel manner the theory of quanta. We end our paper with a comparison between both notions of 'power', stressing some radical differences between them.Comment: Forthcoming in: Probing the Meaning and Structure of Quantum Mechanics, D. Aerts, M.L. Dalla Chiara, C. de Ronde and D. Krause (Eds.), World Scientific, Singapore. arXiv admin note: text overlap with arXiv:1310.453

Modeling of Phenomena and Dynamic Logic of Phenomena

Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, problem or theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models