Analogical modeling (AM) is a memory based model with a documented performance comparable to other types of memory based learning. Known algorithms implementing AM have a computationally complexity of O(2). We formulate a representation theorem on analogical modeling which is used for implementing a range of approximations to AM with a complexity starting as low as O(n).

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Both hierarchical plmming and analogical phmning (:a~l separately reduce the amount of search a planner must perform to find a solution to a partitular problem. We believe that these two methods cazl bc integrated mtd that the combined planner will have ,-m even smaller average search space them either method would produce ahme. In this work: we present our ideas for integrating the two met hod...

Nicholas Barbon’s A discourse of trade presents, in its construction, substance, and rhetoric, an early outline of a new science of the legislator for the new politics of commerce. Barbon drew together economic and political arguments, applying insights from the latter to a new understanding of the political potential of the former. His accounts of the aspect of infinity in economic growth, his...

Previous work on speech-to-speech translation has su ered from problems of brittleness and low quality (rule-based approaches), or from excessive data requirements and linguistic ine ciency (analogical or example-based approaches). In this paper, we present a probabilistic approach to analogical speech translation, and describe its integration with linguistic processing. The evaluation results ...

We propose an approach to developing models of analogical reasoning within production system architectures. Though existing theories of analogy have succeeded in capturing many aspects of analogical behavior, they have several limitations. First, the theories use empirical support that focuses almost exclusively on high-level data that illustrate the results of analogy, ignoring low-level data ...

Research in analogical reasoning suggests that higher-order cognitive functions such as abstract reasoning, far transfer, and creativity are founded on recognizing structural similarities among relational systems. Here we integrate theories of analogy with the computational framework of reinforcement learning (RL). We propose a computational synergy between analogy and RL, in which analogical c...

The present study was aimed at assessing the effect of object similarities on participants' evaluations of analogical quality. Results from an experimental condition in which the relations involved in the compared situations were explicitly highlighted, showed that general object similarities (membership to same category) positively affected the evaluations of analogical quality. In contrast, n...

Analogical learning is based on a twostep inference process: (i) computation of a structural mapping between a new and a memorized situation; (ii) transfer of knowledge from the known to the unknown situation. This approach requires the ability to search for and exploit such mappings, hence the need to properly define analogical relationships, and to efficiently implement their computation. In ...

We present an approach to analogical reasoning which is inherently dependent on abstraction. While typical cognitive and AI models of analogy perform a direct mapping from objects of the base to objects of the target domain, our model performs mapping via abstraction. Abstraction is calculated as most specific generalization of the base and the target structure. In contrast to existing models, ...