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).

Many mathematical proofs are hard to generate for humans and even harder for automated theorem provers. Classical techniques of automated theorem proving involve the application of basic rules, of built-in special procedures, or of tactics. Melis (Melis 1993) introduced a new method for analogical reasoning in automated theorem proving. In this paper we show how the derivational analogy replay ...

ion over analog and new model Spawning of adaptation goals Adaptation strategy 1 SBF model of GTM Adaptation strategy n Adaptation goal GTM Evaluation of new model Retrieval of GTMs Instantiation of GTMs Composition of behaviors Analog Analogical transfer New model New model GTM Figure 3. Model-based analogy in Ideal. .

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simp...

This paper presents the integrated AMBR model of analogy and memory based on the cognitive architecture DUAL. This model was also able to predict new phenomena such as context effects on problem solving and memory distortions as result of double analogies, which effects were then tested and confirmed in psychological experiments.

This paper introduces the various forms of analogy in NARS, a generalpurpose reasoning system. NARS is an AI system designed to be adaptive and to work with insufficient knowledge and resources. In the system, multiple types of inference, including analogy, deduction, induction, abduction, comparison, and revision, are unified both in syntax and in semantics. The system can also carry out relat...

People are habitual explanation generators. At its most mundane, our propensity to explain allows us to infer that we should not drink milk that smells sour; at the other extreme, it allows us to establish facts (e.g., theorems in mathematical logic) whose truth was not even known prior to the existence of the explanation (proof). What do the cognitive operations underlying the inference that t...

We present a methodology for using analogy to derive programs based on a derivational transformat ion method. The derived programs are deductively closed under the rules in the knowledge base, and the emphasis is on speeding up the derivation of a solution. We describe certain heuristics to find a good source analogue to the target problem efficiently, show how the derivation trace of that prog...

I'll show in an initial section (1.) that the kind of analogy between life and information (argue for by authors such as [1], [2], [3] [4], [5], [6]) is like the design argument and that if the design argument is invalid, the argument to the effect that artificial mind may represents an expected advance in the life evolution in Universe is also unfounded and invalid. However, if we are prepared...