Titlebook: Research and Development in Intelligent Systems XXVII; Incorporating Applic Max Bramer,Miltos Petridis,Adrian Hopgood Conference proceeding

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On the Usefulness of Weight-Based Constraints in Frequent Subgraph Mininganalysis of weights in combination with mining for substructures might yield more precise results. In particular, we study frequent subgraph mining in the presence of weight-based constraints and explain how to integrate them into mining algorithms. While such constraints only yield approximate mini

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Induction of Modular Classification Rules: Using Jmax-pruningogether into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even outperform decision trees. Both kinds of algorithms tend to overfit on large and noisy datasets and this has led to the development of pru

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A Kolmogorov Complexity View of Analogy: From Logical Modeling to Experimentationso a creative process. More specific is the notion of analogical proportion like “2 is to 4 as 5 is to 10” or “read is to reader as lecture is to lecturer”: such statements can be precisely described within an algebraic framework. When the proportion holds between concepts as in “engine is to car as

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PIPSS*: A System based on Temporal Estimates and resources to the actions. Currently, most of the real world problems require the use of shared and limited resources with time constraints when planning. Then, systems that can integrate planning and scheduling techniques to deal with this kind of problems are needed..This paper describes the e

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Extending SATPLAN to Multiple Agents agents are able to achieve individual goals that may be either independent, or necessary for the achievement of a global common goal. The agents are able to generate individual plans in order to achieve their own goals, but, as they share the same environment, they need to find a coordinated course

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A New Approach for Partitional Clustering Using Entropy Notation and Hopfield Networkral groups, or clusters, in an unsupervised manner. This . network uses an entropy based energy function to overcome the problem of insufficient understanding of the data and to obtain the optimal parameters for clustering. Additionally, a chaotic variable is introduced in order to escape from the l

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Hierarchical Traces for Reduced NSM Memory Requirementssly published, instance-based reinforcement learning algorithm. A hierarchical memory representation reduces the memory requirements by allowing traces to share common sub-sequences. We present moderated mechanisms for estimating discounted future rewards and for dealing with hidden state using hier

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On Reinforcement Memory for Non-Markovian Controllife stochastic predictions and control problems. Instead of holistic search for the whole memory contents, the controller adopts associated feature analysis to produce the most likely relevant action from previous experiences. Actor-Critic (AC) learning is used to adaptively tune the control parame

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