A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT)[17], Speed-up Robust Feature (SURF)[4], and more recently Binary Robust Invariant Scalable Keypoints (BRISK)[I6] to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise. To best address the current requirements, we propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are thus competitive alternatives to existing keypoints in particular for embedded applications.

/pdf/freak-fast-retina-keypoint-53hq67wxuq.pdf

FREAK: Fast Retina Keypoint

Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

/pdf/a-field-guide-to-genetic-programming-3wkkmsw81n.pdf

A Field Guide to Genetic Programming

https://dialnet.unirioja.es/descarga/articulo/4511387.pdf

The origins of order. Self-organization and selection in evolution

A clutch of '-isms' characterises the approach to consciousness which David Chalmers defends: dualism, epiphenomenalism, functionalism, anti-reductionism, and -probably -panpsychism. (The author would no doubt want 'naturalism' included in the list as well, but as we shall see, Chalmers' predilection to describe his theory as 'scientific' stretches credibility.) While the book does not, as far as I can see, move consciousness research significantly forward, Chalmers succeeds admirably in clarifying the philosophical terrain around and within each of these '-isms' and in questioning the usual assumptions which suggest some of them are mutually exclusive. Because nearly all of what follows is highly critical, I want to be explicit about one thing: I do not think this is a bad book. Throughout, most discussions keep to a very high standard; it's just that they include fatal flaws.

The conscious mind: In search of a fundamental theory

A human observer is able to recognize the color of objects irrespective of the light used to illuminate them. This is called color constancy. A digital camera uses a sensor to measure the reflected light, meaning that the measured color at each pixel varies according to the color of the illuminant. Therefore, the resulting colors may not be the same as the colors that were perceived by the observer. Obtaining color constant descriptors from image pixels is not only important for digital photography, but also valuable for computer vision, color-based automatic object recognition, and color image processing in general. This book provides a comprehensive introduction to the field of color constancy, describing all the major color constancy algorithms, as well as presenting cutting edge research in the area of color image processing. Beginning with an in-depth look at the human visual system, Ebner goes on to: examine the theory of color image formation, color reproduction and different color spaces; discuss algorithms for color constancy under both uniform and non-uniform illuminants; describe methods for shadow removal and shadow attenuation in digital images; evaluate the various algorithms for object recognition and color constancy and compare this to data obtained from experimental psychology; set out the different algorithms as pseudo code in an appendix at the end of the book. Color Constancy is an ideal reference for practising engineers, computer scientists and researchers working in the area of digital color image processing. It may also be useful for biologists or scientists in general who are interested in computational theories of the visual brain and bio-inspired engineering systems.

Color constancy

Evolutionary algorithms apply the process of variation, reproduction, and selection to look for an individual capable of solving the task at hand. In order to improve the evolvability of a population we propose to copy important characteristics of nature's search space. Desired characteristics for a genotype-phenotype mapping are described and several highly redundant genotype-phenotype mappings are analyzed in the context of a population-based search. We show that evolvability, defined as the ability of random variations to sometimes produce improvement, is influenced by the existence of neutral networks in genotype space. Redundant mappings allow the population to spread along the network of neutral mutations and the population is quickly able to recover after a change has occurred. The extent of the neutral networks affects the interconnectivity of the search space and thereby affects evolvability.

How neutral networks influence evolvability

As participants in this Dagstuhl session address the challenge of General Video Game Playing (GVGP), we have recognised the need to create a Video Game Description Language (VGDL). Unlike General Game Playing, we have envisioned GVGP will not require a prescribed language to facilitate understanding of the logic of the game: requiring the computational agent to ascertain these facts for itself. However, we would still require means to define the wide range of problems the GVGP agents may face for the purpose of classification. Not only would such a language provide means to encapsulate the features and mechanics of a game for the purposes of human understanding, but also provide context for the evaluation of GVGP agents having completed playing. Outside of the issues of classification, there is also the opportunity for automatic game generation. Given the intent of the GVGP group to work within a framework akin to the one of the Physical Travelling Salesman Problem (PTSP), we aim to attach a code-base to the VGDL compiler that derives implementations of these games from the definition that can be used in conjunction with GVGP. Implementing such a compiler could provide numerous opportunities; users could modify existing games very quickly, or have a library of existing implementations defined within the language (e.g. an Asteroids ship or a Mario avatar) that have pre-existing, parameterised behaviours that can be customised for the users specific purposes. Provided the language is fit for purpose, automatic game creation could be explored further through experimentation with machine learning algorithms, furthering research in game creation and design. In order for both of these perceived functions to be realised and to ensure it is suitable for a large user base we recognise that the language carries several key requirements. Not only must it be human-readable, but retain the capability to be both expressive and extensible whilst equally simple as it is general. In our preliminary discussions, we sought to define the key requirements and challenges in constructing a new VGDL that will become part of the GVGP process. From this we have proposed an initial design to the semantics of the language and the components required to define a given game. Furthermore, we applied this approach to represent classic games such as Space Invaders, Lunar Lander and Frogger in an attempt to identify potential problems that may come to light. In summary, our group has agreed on a series of preliminary language components and started to experiment with forms of implementation for both the language and the attached framework. In future we aim to realise the potential of the VGDL for the purposes of Procedural Content Generation, Automatic Game Design and Transfer Learning and how the roadmap for GVGP can provide opportunities for these areas.

https://drops.dagstuhl.de/opus/volltexte/2013/4338/pdf/9.pdf

Towards a Video Game Description Language

Light, which is reflected from an object, varies with the type of illuminant used. Nevertheless, the color of an object appears to be approximately constant to a human observer. The ability to compute color constant descriptors from reflected light, is called color constancy. In order to solve the problem of color constancy, some assumptions have to be made. One frequently made assumption is that on average, the world is gray. We address the problem of color constancy and focus on the use of space average color for color constancy. Instead of computing global space average color we suggest to use local space average color as the illuminant frequently varies across an image. We discuss several different methods on how to compute local space average color. The performance of the different algorithms as well as related algorithms is evaluated on an object recognition task. Algorithms based on local space average color are simple, yet highly effective for the problem of color constancy. Such algorithms are particularly suited for object recognition tasks.

Color constancy based on local space average color

One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP). In GVGP, computational agents will be asked to play video games that they have not seen before. At the minimum, the agent will be given the current state of the world and told what actions are applicable. Every game tick the agent will have to decide on its action, and the state will be updated, taking into account the actions of the other agents in the game and the game physics. We envisage running a competition based on GVGP playing, using arcadestyle (e.g. similar to Atari 2600) games as our starting point. These games are rich enough to be a formidable challenge to a GVGP agent, without introducing unnecessary complexity. The competition that we envisage could have a number of tracks, based on the form of the state (frame buffer or object model) and whether or not a forward model of action execution is available. We propose that the existing Physical Travelling Salesman (PTSP) software could be extended for our purposes and that a variety of GVGP games could be created in this framework by AI and Games students and other developers. Beyond this, we envisage the development of a Video Game Description Language (VGDL) as a way of concisely specifying video games. For the competition, we see this as being an interesting challenge in terms of deliberative search, machine learning and transfer of existing knowledge into new domains.

/pdf/general-video-game-playing-2fkyava99g.pdf

Marc Ebner

Papers

Color constancy

How neutral networks influence evolvability

Towards a Video Game Description Language

Color constancy based on local space average color

General video game playing