Computational Creativity: Coming of Age

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 recently introduced series of description logics under the common moniker `DL-Lite' has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique same assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.

Computational techniques have shown much promise in the field of Finance, owing to their ability to extract sense out of dauntingly complex systems. This paper reviews the most promising of these techniques, from traditional computational intelligence methods to their machine learning siblings, with particular view to their application in optimising the management of a portfolio of financial instruments. The current state of the art is assessed, and prospective further work is assessed and recommended

This paper reviews the functional aspects of statistical learning theory. The main point under consideration is the nature of the hypothesis set when no prior information is available but data. Within this framework we first discuss about the hypothesis set: it is a vectorial space, it is a set of pointwise defined functions, and the evaluation functional on this set is a continuous mapping. Based on these principles an original theory is developed generalizing the notion of reproduction kernel Hilbert space to non hilbertian sets. Then it is shown that the hypothesis set of any learning machine has to be a generalized reproducing set. Therefore, thanks to a general "representer theorem", the solution of the learning problem is still a linear combination of a kernel. Furthermore, a way to design these kernels is given. To illustrate this framework some examples of such reproducing sets and kernels are given.

As fragments of first-order logic, Description logics (DLs) do not provide nonmonotonic features such as defeasible inheritance and default rules. Since many applications would benefit from the availability of such features, several families of nonmonotonic DLs have been developed that are mostly based on default logic and autoepistemic logic. In this paper, we consider circumscription as an interesting alternative approach to nonmonotonic DLs that, in particular, supports defeasible inheritance in a natural way. We study DLs extended with circumscription under different language restrictions and under different constraints on the sets of minimized, fixed, and varying predicates, and pinpoint the exact computational complexity of reasoning for DLs ranging from ALC to ALCIO and ALCQO. When the minimized and fixed predicates include only concept names but no role names, then reasoning is complete for NExpTime^NP. It becomes complete for NP^NExpTime when the number of minimized and fixed predicates is bounded by a constant. If roles can be minimized or fixed, then complexity ranges from NExpTime^NP to undecidability.

The report gives an overview about activities on the topic Semantic Web. It has been released as technical report for the project "KTweb -- Connecting Knowledge Technologies Communities" in 2003.

Considerable progress has been made over the last 15 years on building adaptive control systems to assist pilots in flying damaged aircraft. Once a pilot has regained control of a damaged aircraft, the next problem is to determine the best site for an emergency landing.  In general, the decision depends on many factors including the actual control envelope of the aircraft, distance to the site, weather en route, characteristics of the approach path, characteristics of the runway or landing site, and emergency facilities at the site.  All of these influence the risk to the aircraft, to the passengers and crew, and to people and property on the ground.  We describe an emergency landing planner that takes these various factors into consideration and proposes possible routes and landing sites to the pilot, ordering them according to estimated risk.   We give an overview of the system architecture and input data, describe our modeling of risk, describe how we search the space of landing sites and routes, and give a preliminary performance assessment for characteristic emergency scenarios using the current research prototype.

In this paper, we present a prototype application — the Technology Trend Tracker — to measure the reality of technology trends of interest using information on the Web to inform decisions such as when to develop training, when to invest in expertise, and more. This prototype performs this task by integrating several artificial intelligence technologies in an innovative way. These technologies include rich semantic representations, a natural language understanding module, and a flexible semantic matcher. We use our system to augment Accenture's annual technology vision survey and show how our system performs well on measuring the reality of technology trends from this survey. We also show why our system performs well through an ablation study.

The goal of the Innovative Applications of Artificial Intelligence (IAAI) conference is to highlight new, innovative, systems and application areas of AI technology and to point out the often-overlooked difficulties involved in deploying complex technology to end users. Those of us who have ventured out of the realm of pure research and tried to build applications to be used by our fellow humans realize that it takes a lot more than just brilliant algorithms to make an application survive in the real world. Each application that succeeds is worth celebrating and the teams behind them are due wholehearted congratulations. It is in this spirit that we bring you this special issue covering select applications from the IAAI conference held last year in Chicago.