An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

13 EADERS of Mr. Bertrand Russell's philosophical £v works know that one of his pupils before the outbreak of the war, an Austrian, Mr. Ludwig Wittgenstein, caused him to change his views in some important particulars. Curiosity can now be satisfied. The “Tractatus Logico-Philosophicus “which Mr. Ogden has included in his new library of philosophy is a remarkable and strikingly original work. It is published in German and English in parallel pages. It is difficult to appreciate the reason for this, seeing that the author is evidently familiar with our language and has himself carefully revised the proofs of the translation. Also we should have liked to have the Tractatus without Mr. Russell's Introduction, not, we hasten to add, on account of any fault or shortcoming in that introduction, which is highly appreciative and in part a defence of himself, in part explanatory of the author, but for the reason that good wine needs no bush and that Mr. Russell's bush has the unfortunate effect of dulling the palate instead of whetting the appetite. In his penultimate sentence Mr. Russell says; “To have constructed a theory of logic which is not at any point obviously wrong is to have achieved a work of extraordinary difficulty and importance.” We agree, but how uninspiring when compared with Mr. Wittgenstein's own statement of aim: “What can be said at all can be said clearly, and whereof one cannot speak, thereof one must be silent.” Tractatus Logico-Philosophicus. By Ludwig Wittgenstein. (International Library of Psychology, Philosophy and Scientific Method.) Pp. 189. (London: Kegan Paul and Co., Ltd.; New York: Harcourt, Brace and Co., Inc., 1922.) 10s. 6d. net.

Tractatus Logico-Philosophicus

In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.

The Soar Cognitive Architecture

Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Metaheuristic research: a comprehensive survey

Lectures or the Elementary Psychology of Feeling and Attention

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of `strong' computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam's (Representation & Reality, Bradford Books, Cambridge in 1988) monograph, "Representation & Reality", which if correct, has important implications for turing machine functionalism and the prospect of `conscious' machines. In the paper, instead of seeking to develop Putnam's claim that, "everything implements every finite state automata", I will try to establish the weaker result that, "everything implements the specific machine Q on a particular input set (x)". Then, equating Q (x) to any putative AI program, I will show that conceding the `strong AI' thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere.

Why Computers Can't Feel Pain

This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) [4] to the Particle Swarm Optimiser (PSO) metaheuristic [22], effectively merging the two swarm intelligence algorithms The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs

https://eprints.gold.ac.uk/6884/1/fp151%2Dal%2DrifaiePS_final.pdf

An investigation into the merger of stochastic diffusion search and particle swarm optimisation

In the spirit of Searle’s definition of weak and strong artificial intelligence, this paper presents a discussion on weak computational creativity in swarm intelligence systems It addresses the concepts of freedom and constraint and their impact on the creativity of the underlying systems An analogy is drawn on mapping these two ‘prerequisites’ of creativity onto the two well-known phases of exploration and exploitation in swarm intelligence algorithms, followed by the visualisation of the behaviour of the swarms whose performance are evaluated in the context of arguments presented The paper also discusses that the strong computational creativity is presented in ways emphasising that genuine creativity implies ‘genuine understanding’ and other cognitive states, along with autonomy—asserting that without ‘Strong Embodiment’, computational systems are not genuinely autonomous

/pdf/weak-and-strong-computational-creativity-3wosmhqz2d.pdf

Weak and Strong Computational Creativity

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’.

/pdf/cooperation-of-nature-and-physiologically-inspired-2ucspn2yiq.pdf

Cooperation of Nature and Physiologically Inspired Mechanisms in Visualisation

This paper touches upon the philosophical concept of evil in the context of creativity in general, and computational creativity in particular. In this work, dark creativity is introduced and linked to two important pre-requisites of creativity i.e. freedom and constraints. A hybrid computational system is then presented; it includes one swarm intelligence algorithm, Stochastic Diffusion Search – mimicking the foraging behaviour of one species of ant, Leptothorax acervorum – and one physiological mechanism – imitating the behaviour of human immunodeficiency virus. The aim is to outline an integration strategy deploying the search capabilities of the swarm intelligence algorithm and the destructive power of the digital virus. The swarm intelligence algorithm determines the colour attribute of the dynamic areas of interest within the input image, and the digital virus modifies the state of the input image, creating the projection of “evil” over time evil is used here as excessive use of underlying freedom. The paper concludes by exploring the significance of sensorimotor couplings and the impact of intentionality and genuine understanding of computational systems in the light of the philosophical concept of weak and strong computational creativity.

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2016.1151862

Mark Bishop

Papers

Why Computers Can't Feel Pain

An investigation into the merger of stochastic diffusion search and particle swarm optimisation

Weak and Strong Computational Creativity

Cooperation of Nature and Physiologically Inspired Mechanisms in Visualisation

On evil and computational creativity