Over the centuries, it has become reified in that analogical reasoning has sometimes been regarded as a fundamental cognitive process. In addition, it has become identified with a particular expressive format. Beyond this dependence, research in cognitive science suggests that analogy relies on a number of genuinely fundamental cognitive capabilities, including semantic flexibility, the perception of resemblances and of distinctions, imagination, and metaphor. Extant symbolic models of analogical reasoning have various sorts of limitation, yet each model presents some important insights and plausible mechanisms.
Analogy is heavily used in instructional texts. We introduce the concept of analogical dialogue acts (ADAs), which represent the roles utterances play in instructional analogies. We describe a catalog of such acts, based on ideas from structure-mapping theory. We focus on the operations that these acts lead to while understanding instructional texts, using the Structure-Mapping Engine (SME) and dynamic case construction in a computational model. We test this model on a small corpus of instructional analogies expressed in simplified English, which were understood via a semi-automatic natural language system using analogical dialogue acts. The model enabled a system to answer questions after understanding the analogies that it was not able to answer without them.
This paper describes an analogy ontology, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's structure-mapping theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Introduction There is mounting psychological evidence that human cognition centrally involves similarity computations over structured representations, in tasks ranging from high-level visual perception to problem solving, learning, and conceptual change . Understanding how to integrate analogical processing into AI systems seems crucial to creating more humanlike reasoning systems .
Analogy-based Story Generation (ASG) is a relatively under-explored approach for story generation and computational narrative. In this paper, we present the SAM (Story Analogies through Mapping) algorithm as our attempt to expand the scope and complexity of stories generated by ASG. Comparing with existing work and our prior work, there are two main contributions of SAM: it employs 1) analogical reasoning both at the specific story content and general domain knowledge levels, and 2) temporal reasoning about the story (phase) structure in order to generate more complex stories. We illustrate SAM through a few example stories.
Understanding methods of problem solving is a main goal of both Cognitive Science and Artificial Intelligence. Although general problem solvers that use weak methods have been developed, they are not sufficient for reasoning about complex problems in complicated domains. For such tasks, the search is generally intractable: the height of the search tree is determined by the complexity of the solution, and the branching factor at each level is determined by the large number of applicable operators. The principle problem is that weak methods operating on large domain theories provide the problem solver with only a very limited notion of which operators might be relevant to which goals. What is needed to solve this problem, then, is a set of learning methods that can select and retrieve past experiences that are relevant to the current goal.