As an alternative to previous studies on extracting class attributes from unstructured text, which consider either Web documents or query logs as the source of textual data, A bootstrapped method extracts class attributes simultaneously from both sources, using a small set of seed attributes. The method improves extraction precision and also improves attribute relevance across 40 test classes.

Kernel-based Bayesian methods for Reinforcement Learning (RL) such as Gaussian Process Temporal Difference (GPTD) are particularly promising because they rigorously treat uncertainty in the value function and make it easy to specify prior knowledge. However, the choice of prior distribution significantly affects the empirical performance of the learning agent, and little work has been done extending existing methods for prior model selection to the online setting. This paper develops Replacing-Kernel RL, an online model selection method for GPTD using sequential Monte-Carlo methods. Replacing-Kernel RL is compared to standard GPTD and tile-coding on several RL domains, and is shown to yield significantly better asymptotic performance for many different kernel families. Furthermore, the resulting kernels capture an intuitively useful notion of prior state covariance that may nevertheless be difficult to capture manually.

Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be efficient on complex problems, few empirical studieshave been conducted in such domains. In this paper, three neural network representations, a direct encoding, a complexifying encoding, and an implicit encoding capable of adapting the genotype-phenotype mapping are compared on Nothello, a complex game playing domain from the AAAI General Game Playing Competition. Implicit encoding makes the search more efficient and uses several times fewer parameters. Random mutation leads to highly structured phenotypic variation that is acquired during the course of evolution rather than built into the representation itself. Thus, adaptive representations learn to become evolvable, and furthermore do so in a way that makes search efficient on difficult coevolutionary problems.

Evolutionary algorithms tend to produce solutions that are not evolvable: Although current fitness may be high, further search is impeded as the effects of mutation and crossover become increasingly detrimental. In nature, in addition to having high fitness, organisms have evolvable genomes: phenotypic variation resulting from random mutation is structured and robust. Evolvability is important because it allows the population to produce meaningful variation, leading to efficient search. However, because evolvability does not improve immediate fitness, it must be selected for indirectly. One way to establish such a selection pressure is to change the fitness function systematically. Under such conditions, evolvability emerges only if the representation allows manipulating how genotypic variation maps onto phenotypic variation and if such manipulations lead to detectable changes in fitness. This research forms a framework for understanding how fitness function and representation interact to produce evolvability. Ultimately evolvable encodings may lead to evolutionary algorithms that exhibit the structured complexity and robustness found in nature.