A hypercomputational approach to the agent causation theory of free will

Download
2006
Mersin, Serhan
Hypercomputation, which is the general concept embracing all machinery capable of carrying out more tasks than Turing Machines and beyond the Turing Limit, has implications for various fields including mathematics, physics, computer science and philosophy. Regarding its philosophical aspects, it is necessary to reveal the position of hypercomputation relative to the classical computational theory of mind in order to clarify and broaden the scope of hypercomputation so that it encompasses some phenomena which are regarded as problematic because of their property of being uncomputable. This thesis points to a relation between hypercomputation and the agent-causation theory of free will by exploring that theory's alleged infinite-regress feature, which has been regarded by some authors as problematic and used against the agent causation theory. In order to cope with this problem, we propose a certain hypercomputer, viz. the reverse Zeus machine. The reverse Zeus machine can help to understand the infinite-regress aspect of agent causation better than accelerating Turing machines (or ordinary Zeus machines). Accelerating Turing machines are abstract machines which perform temporal patterning in an accelerating manner by executing each step in half the time required for the previous step. This allows them to compute infinitely many operations in finite time. Although reverse Zeus machines have the same working principle as accelerating Turing machines, we show that agent causation can be represented by reverse Zeus machines better than by the classical Zeus machines.

Suggestions

A mathematical contribution of statistical learning and continuous optimization using infinite and semi-infinite programming to computational statistics
Özöğür-Akyüz, Süreyya; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009)
A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to “learn”. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis,...
Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes
Vural, Hülya; Alpaslan, Ferda Nur; Department of Computer Engineering (2004)
The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlow...
Evolving aggregation behaviors for swarm robotics systems: a systematic case study
Bahçeci, Erkin; Şahin, Erol; Department of Computer Engineering (2005)
Evolutionary methods are shown to be useful in developing behaviors in robotics. Interest in the use of evolution in swarm robotics is also on the rise. However, when one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding some parameters of fitness evaluations and of the genetic algorithm. In this thesis, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron control...
New montgomery modular multıplier architecture
Çiftçibaşı, Mehmet Emre; Yücel, Melek D; Department of Electrical and Electronics Engineering (2005)
This thesis is the real time implementation of the new, unified field, dualا radix Montgomery modular multiplier architecture presented by Savaş et al, for performance comparison with standard Montgomery multiplication algorithms. The unified field architecture operates in both GF(p) and GF(2n). The dual radix capability enables processing of two bits of the multiplier in every clock cycle in GF(2n) mode, while one bit of the multiplier is processed in GF(p) mode. The new architecture is implemented in a Xi...
Moving object identification and event recognition in video surveillance systems
Örten, Burkay Birant; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2005)
This thesis is devoted to the problems of defining and developing the basic building blocks of an automated surveillance system. As its initial step, a background-modeling algorithm is described for segmenting moving objects from the background, which is capable of adapting to dynamic scene conditions, as well as determining shadows of the moving objects. After obtaining binary silhouettes for targets, object association between consecutive frames is achieved by a hypothesis-based tracking method. Both of t...
Citation Formats
S. Mersin, “A hypercomputational approach to the agent causation theory of free will,” M.S. - Master of Science, Middle East Technical University, 2006.