Prime implicates and prime implicants have proven relevant to a number of areas of artificial intelligence, most notably abductive reasoning and knowledge compilation. The purpose of this paper is to examine how these notions might be appropriately extended from propositional logic to the modal logic K. We begin the paper by considering a number of potential definitions of clauses and terms for K. The different definitions are evaluated with respect to a set of syntactic, semantic, and complexity-theoretic properties characteristic of the propositional definition. We then compare the definitions with respect to the properties of the notions of prime implicates and prime implicants that they induce. While there is no definition that perfectly generalizes the propositional notions, we show that there does exist one definition which satisfies many of the desirable properties of the propositional case. In the second half of the paper, we consider the computational properties of the selected definition. To this end, we provide sound and complete algorithms for generating and recognizing prime implicates, and we show the prime implicate recognition task to be PSPACE-complete. We also prove upper and lower bounds on the size and number of prime implicates. While the paper focuses on the logic K, all of our results hold equally well for multi-modal K and for concept expressions in the description logic ALC.

ICAART 2010 will be held January 22-24, 2010, in Valencia, Spain. International Conference on Intelligent This page includes forthcoming AAAI sponsored conferences, conferences presented User Interfaces. IUI 2010 will be by AAAI Affiliates, and conferences held in cooperation with AAAI. AI held February 7-10, 2010, in Hong Magazine also maintains a calendar listing that also includes nonaffiliated Kong. ICEIS 2010 will be held June 8-12, The International RuleML Symposium Stanford, California.

Creativity isn’t magical. It’s an aspect of normal human intelligence, not a special faculty granted to a tiny elite. There are three forms: combinational, exploratory, and transformational. All three can be modeled by AI—in some cases, with impressive results. AI techniques underlie various types of computer art. Whether computers could “really” be creative isn’t a scientific question but a philosophical one, to which there’s no clear answer. But we do have the beginnings of a scientific understanding of creativity.

Despite the fact that AI has always been adventurous in trying to elucidate complex aspects of human behaviour, only recently has there been research into computational modelling of humor. One obstacle to progress is the lack of a precise and detailed theory of how humor operates. Nevertheless, since the early 1990s, there have been a number of small programs that create simple verbal humor, and more recently there have been studies of the automatic classification of the humorous status of texts. In addition, there are a number of advocates of the practical uses of computational humor: in user-interfaces, in education, and in advertising. Computer-generated humor is still quite basic, but it could be viewed as a form of exploratory creativity. For computational humor to improve, some hard problems in AI will have to be addressed.

This paper deals with computational approaches to storytelling, or the production of stories by computers, with a particular attention on the way human creativity is modelled or emulated, also in computational terms. Features relevant to creativity and to stories are analysed, and existing systems are reviewed under the light of that analysis.The extent to which they implement the key features proposed in recent models of computational creativity is discussed. Limitations, avenues of future research and expected trends are outlined.

Such creative software can be used for autonomous creative tasks, such as inventing mathematical theories, writing poems, painting pictures, and composing music. However, computational creativity studies also enable us to understand human creativity and to produce programs for creative people to use, where the software acts as a creative collaborator rather than a mere tool. Historically, it's been difficult for society to come to terms with machines that purport to be intelligent and even more difficult to admit that they might be creative. For instance, in 1934, some professors at the University of Manchester in the United Kingdom built meccano models that were able to solve some mathematical equations. Groundbreaking for its time, this project was written up in a piece in Meccano Magazine. The article was titled "Are Thinking Machines Possible" and was very upbeat, but surprisingly ends by stating that "Truly creative thinking of course will always remain beyond the power of any machine." Surely, though, this attitude has changed in light of the amazing advances in hardware and software technology that followed those meccano models?

The article is about AI research in the context of a complex artistic behavior: expressive music performance. A computer program is presented that learns to play piano with 'expression' and that even won an international computer piano performance contest. A superficial analysis of an expressive performance generated by the system seems to suggest creative musical abilities. After a critical discussion of the processes underlying this behavior, we abandon the question of whether the system is really creative, and turn to the true motivation that drives this research: to use AI methods to investigate and better understand music performance as a human creative behavior. A number of recent and current results from our research are briefly presented that indicate that machines can give us interesting insights into such a complex creative behavior, even if they may not be creative themselves.

The difference between comedians and their audience is a matter not of kind, but of degree, a difference that is reflected in the vocational emphasis they place on humor. Researchers in the field of computational creativity find themselves in a similar situation. As a subdiscipline of artificial intelligence, computational creativity explores theories and practices that give rise to a phenomenon, creativity, that all intelligent systems, human or machine, can legitimately lay claim to. Who is to say that a given AI system is not creative, insofar as it solves nontrivial problems or generates useful outputs that are not hard wired into its programming? As with comedians' being funny, the difference between studying computational creativity and studying artificial intelligence is one of emphasis rather than one of kind: the field of computational creativity, as typified by a long-running series of workshops at AIrelated conferences, places a vocational emphasis on creativity and attempts to draw together the commonalities of what

When configuring customizable software, it is useful to provide interactive tool-support that ensures that the configuration does not breach given constraints. But, when is a configuration complete and how can the tool help the user to complete it? We formalize this problem and relate it to concepts from non-monotonic reasoning well researched in Artificial Intelligence. The results are interesting for both practitioners and theoreticians. Practitioners will find a technique facilitating an interactive configuration process and experiments supporting feasibility of the approach. Theoreticians will find links between well-known formal concepts and a concrete practical application.

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.