Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

‘The Production of Space’, in: Frans Jacobi, Imagine, Space Poetry, Copenhagen, 1996, unpaginated.

The Production of Space

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.

/pdf/a-survey-of-outlier-detection-methodologies-mpua0o09er.pdf

A Survey of Outlier Detection Methodologies

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

This paper presents a possible future direction for agent-based simulation using complex agents that can learn from experience and report their individual evaluations. Adding learning to the agent model permits the simulation of potentially important agent behavior such as curiosity. The agents can then report evaluations of a design that are situated in their individual experience. The paper describes the architecture of curious agents used in the situated evaluation of designs. It then describes an example of the application of such curious agents in the evaluation of the curating of an exhibition in an art gallery.

https://cs.gmu.edu/~jgero/publications/2002/02SaundersGeroWAID.pdf

Curious agents and situated design evaluations

This paper presents a computational model of creativity that attempts to capture within a social context an important aspect of the art and design process: the search for novelty. The computational model consists of multiple novelty-seeking agents that can assess the interestingness of artworks. The agents can communicate to particularly interesting artworks to others. Agents can also communicate to reward other agents for finding interesting artworks. We present the results from running experiments to investigate the effects of searching for different degrees of novelty on the artworks produced and the social organisation of the agents.

/pdf/the-digital-clockwork-muse-a-computational-model-of-3vgjf4knaz.pdf

The digital clockwork muse: a computational model of aesthetic evolution

This paper reviews the long-standing debate surrounding the nature of machine intelligence, autonomy and creativity and argues for an approach to developing autonomous computational creativity that models personal motivations, social interactions and the evolution of domains. The implications of this argument on the types of cognitive processes that are required for the development of autonomous computational creativity are explored and a possible approach to achieving the goal is described. In particular, this paper describes the development of artificial creative systems composed of intrinsically motivated agents engaging in language games to interact with a shared social and cultural environment. The paper discusses the implications that this type of approach may have for the development of autonomous creative systems.

Towards Autonomous Creative Systems: A Computational Approach

In this paper, we describe a novel approach to developing computational models of creativity that supports the multiple approaches to the study of artificial creative systems. The artificial creativity approach to the development of computational models of creative systems is described with reference to Csikszentmihalyi's systems view of creativity. Some interesting results from studies using an early implementation of an artificially creative system, The Digital Clockwork Muse, are presented. The different studies show how the artificial creativity approach supports the study of creativity from a variety of standpoints that mirror the disciplines that study human creativity. The use of artificial creativity allows these different studies to be conveniently conducted on the same computational model and integrated in to a more complete picture of the creative process.

/pdf/how-to-study-artificial-creativity-3la4c509n7.pdf

How to study artificial creativity

It is our belief that modelling the behaviour of stem cells in the adult human body as an agent-based system is the most appropriate way of understanding the process of self-organisation. We have undertaken several case studies where formal and/or computational models of stem cell systems, have been re-developed using an agent-based approach. This paper presents details of one of these case studies where we have used an agent-based approach as opposed to a cellular automata approach. A formalisation of the non-agent and agent-based approach is given, and from the results of this investigation, we aim to demonstrate the advantages of the agent-based approach for developing biologically plausible models with emergent self-organising dynamics. The aim of this paper first to discuss the importance of modelling and simulating stem cells, because of certain experimental limitations, but also to demonstrate that the multi-agent approach to modelling is the most appropriate.

Rob Saunders

Papers

Curious agents and situated design evaluations

The digital clockwork muse: a computational model of aesthetic evolution

Towards Autonomous Creative Systems: A Computational Approach

How to study artificial creativity

Agent-based modelling of stem cell self-organisation in a niche