The marketplace for definitions and theories of creativity is crowded indeed. No hard consensus exists on the elements of an all-embracing theory, or on what specific sub-processes and representations are required to support creativity, either in humans or in machines. Yet commonalities do exist across theories: a search for novelty and utility is implied by most theories, as is the notion that an innovation can be considered creative only if it is not too novel, and can be adequately grounded in the familiar and the understandable. Computational creativity (CC) is the pursuit of creative behavior in machines, and seeks inspiration from both AI and from human psychology. As a practical engineering endeavor, CC can afford to adopt a cafeteria approach to theories of creativity, taking what it needs from different theories and frameworks. In this paper we present a vision for CC research in the age of the Web, in which CC is provided on tap, via a suite of Web services, to any third-party application that needs it. We argue that this notion of Creativity as a Service – which is already a popular business model for human organizations – will allow CC researchers and developers to build ad-hoc mash-ups of whatever processes and representations are most suited to a given application. By offering CC as a centralized service, we can collect statistics on the most useful mash-ups, and therein obtain a new empirical basis for theorizing about creativity in humans and in machines.

We consider a cross-cultural interaction scenario where a group of soldiers assisted by robots interact with local vendors in a market place. We develop a model to quantify, analyze and predict the perception of the actions of the soldiers and the robot by the local population. The model assumes that humans are considering collections of concrete and intangible values which are not, in general, directly and linearly convertible into each other. We argue that satisfactory modeling accuracy can be achieved by restricting the considered intangibles to a small set of {\em culture sanctioned social values}. For these values, the culture provides a name, calculation methods, as well as associated rules of conduct. We validate our model by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. We use the model to evaluate the tradeoffs between several long term strategies to maintain security as well as to increase the trust and goodwill of the local population.

Having discovered a decision principle for a well-known prima facie duty theory in biomedical ethics to resolve particular cases of a common type of ethical dilemma, we developed three applications: a medical ethics advisor system, a medication reminder system and an instantiation of this system in a Nao robot. We are now developing a general, automated method for generating from scratch the ethics needed for a machine to function in a particular domain, without making the assumptions used in our prototype systems.

How obliged can we be to AI, and how much danger does it pose us? A surprising proportion of our society holds exaggerated fears or hopes for AI, such as the fear of robot world conquest, or the hope that AI will indefinitely perpetuate our culture. These misapprehensions are symptomatic of a larger problem—a confusion about the nature and origins of ethics and its role in society. While AI technologies do pose promises and threats, these are not qualitatively different from those posed by other artifacts of our culture which are largely ignored: from factories to advertising, weapons to political systems. Ethical systems are based on notions of identity, and the exaggerated hopes and fears of AI derive from our cultures having not yet accommodated the fact that language and reasoning are no longer uniquely human. The experience of AI may improve our ethical intuitions and self-understanding, potentially helping our societies make better-informed decisions on serious ethical dilemmas.

The three laws of Robotics first appeared together in Isaac Asimov's story 'Runaround' after being mentioned in some form or the other in previous works by Asimov. These three laws commonly known as the three laws of robotics are the earliest forms of depiction for the needs of ethics in Robotics. In simplistic language Isaac Asimov is able to explain what rules a robot must confine itself to in order to maintain societal sanctity. However, even though they are outdated they still represent some of our innate fears which are beginning to resurface in present day 21st Century. Our society is on the advent of a new revolution; a revolution led by advances in Computer Science, Artificial Intelligence & Nanotechnology. Some of our advances have been so phenomenal that we surpassed what was predicted by the Moore's law. With these advancements comes the fear that our future may be at the mercy of these androids. Humans today are scared that we, ourselves, might create something which we cannot control. We may end up creating something which can not only learn much faster than anyone of us can, but also evolve faster than what the theory of evolution has allowed us to. The greatest fear is not only that we might lose our jobs to these intelligent beings, but that these beings might end up replacing us at the top of the cycle. The public hysteria has been heightened more so by a number of cultural works which depict annihilation of the human race by robots. Right from Frankenstein to I, Robot mass media has also depicted such issues. This paper is an effort to understand the need for ethics in Robotics or simply termed as Roboethics. This is achieved by the study of artificial beings and the thought being put behind them. By the end of the paper, however, it is concluded that there isn't a need for ethical robots but more so ever a need for ethical roboticists.

The Reflexive Game Theory (RGT) has been recently proposed by Vladimir Lefebvre to model behavior of individuals in groups. The goal of this study is to introduce the Inverse task. We consider methods of solution together with practical applications. We present a brief overview of the RGT for easy understanding of the problem. We also develop the schematic representation of the RGT inference algorithms to create the basis for soft- and hardware solutions of the RGT tasks. We propose a unified hierarchy of schemas to represent humans and robots. This hierarchy is considered as a unified framework to solve the entire spectrum of the RGT tasks. We conclude by illustrating how this framework can be applied for modeling of mixed groups of humans and robots. All together this provides the exhaustive solution of the Inverse task and clearly illustrates its role and relationships with other issues considered in the RGT.

General Intelligence, was held March 6-9 in Arlington, Virginia. Pascal Hitzler chaired the program committee. The first day of the conference featured in-depth tutorials on leading AGI systems and approaches, including introductions to the SOAR, Texai, and OpenCog software, and overviews of the logic-based, reinforcement learning and program-induction approaches to AGI. Following this, the main conference on Saturday and Sunday featured a number of themed sessions: Evaluation and Metrics (chaired by John Laird), Robotics and Embodiment (chaired by Itamar Arel), Cognitive Architectures (chaired by Pei Wang and Stephen Reed), Logical Approaches to AGI (chaired by Selmer Bringsjord), Learning and Reasoning (chaired by Selmer Bringsjord), Speech and Language (chaired by Moshe Looks), and Self-Awareness and Consciousness (chaired by Ben Goertzel). There were fewer industry participants because in early 2009 (due to the global economic crisis) many U.S. firms were restricting On the other hand there was an Emanuel Kitzelmann, Martin Hofmann, and Ute even greater international participation, including Schmid, from the Cognitive Systems Group at the a keynote speech by Juergen Schmidhuber (from University of Bamberg, who work in the AI tradition IDSIA, in Lugano, Switzerland, and the Technical of "inductive programing." Their paper University of Munich) and a large number of presentations described a clever way to reformulate the conclusions from German researchers.

The theme of this article is the dynamics of evolution of agents. That theme is applied to the evolution of constraint satisfaction, of agents themselves, of our models of agents, of artificial intelligence and, finally, of the Association for the Advancement of Artificial Intelligence (AAAI). The overall thesis is that constraint satisfaction is central to proactive and responsive intelligent behavior.

The Kiva warehouse-management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.

The newly emerging field of machine ethics (Anderson and Anderson 2006) is concerned with adding an ethical dimension to machines. Unlike computer ethics -- which has traditionally focused on ethical issues surrounding humans' use of machines -- machine ethics is concerned with ensuring that the behavior of machines toward human users, and perhaps other machines as well, is ethically acceptable. In this article we discuss the importance of machine ethics, the need for machines that represent ethical principles explicitly, and the challenges facing those working on machine ethics. We also give an example of current research in the field that shows that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of correct ethical judgments and use that principle to guide its own behavior.