The construction of Bayesian Networks (BNs) to model large-scale real-life problems is challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs). These provide modellers with the ability to define classes and construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. In the OO programming paradigm, a key concept is inheritance, the ability to derive attributes and behavior from pre-existing classes, which enables an even higher level of reusability and scalability. However, inheritance in OOBNs has yet to be fully defined and implemented. Here we present iOOBN, a tool which provides fully defined inheritance for OOBNs. We provide guidance on modelling in iOOBN, describe our prototype implementation with an existing BN software tool, Hugin, and demonstrate its applicability and usefulness via a case study of re-engineering an existing large complex dynamic OOBN.

The evolution of cognition is a relatively under-explored issue in cognitive science and evolution theory. The development and influence of evolutionary psychology in recent decades has stimulated interest in it just recently, but the methods applied largely remain bound to ethological observation and the theory-based use of evolutionary principles. Here we illustrate a new empirical approach to answering a particular question that arises in the evolution of decision making. In particular, we show how agent-based evolutionary simulation can answer questions in the evolution of cognition by answering a question about the evolution of utility raised by evolutionary economics, namely how do utilities and prediction co-evolve?

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

The symbolic approach to artificial intelligence research has dominated AI until recent times. It continues to dominate work in the areas of inference and reasoning in artificial systems. The author argues, however, that non-quantitative methods are inherently insufficient for supporting inductive inference. In particular there are reasons to believe that purely deductive techniques—as advocated by the naive physics community—and their nonmonotonic progeny are insufficient for supplying means for the development of the autonomous intelligence that AI has as its primary goal. The lottery paradox points to fundamental difficulties for any such non-quantitative approach to AI. Here, it is suggested that a hybrid system employing both quantitative and non-quantitative modes of reasoning is the most promising avenue for developing an intelligence that can avoid both the paralysis induced by computational complexity and the inductive paralysis to which purely symbolic approaches succumb.

John Searle has used his Chinese room example to attack the idea of computationally reproducing intelligence. His arguments have variously assumed or (more recently) asserted that consciousness and intelligence are necessarily interdependent. This stance has allowed him to apply intuitive arguments about what could or could not be conscious to the issue of what could or could not be intelligent. I present a variety of arguments, theoretical and intuitive, to show that Searle is conflating mentality and semantics. By maintaining that distinction we can then address how to generate the semantics that intelligence requires. In Stevan Hamad's approach to symbol-grounding we have a plausible candidate for finding referential semantics without taking detours through an unanalysable consciouness. Artificial intelligence as normally construed does not require that philosophical problems about consciousness be resolved, let alone that consciousness should be computationally definable: Searle's arguments against strong AI are irrelevant to real-world AI.