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.

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.

Computational narrative is a complex and interesting domain for exploring AI techniques that algorithmically analyze, understand, and most importantly, generate stories. This paper studies the importance of domain knowledge in story generation, and particularly in analogy-based story generation (ASG). Based on the construct of knowledge container in case-based reasoning, we present a theoretical framework for incorporating domain knowledge in ASG. We complement the framework with empirical results in our existing system Riu.

We have explored the use of analogy as a general approach to near and far transfer learning in domains ranging from physics problem solving to strategy games (Klenk and Forbus 2007; Hinrichs and Forbus 2007). Using the same basic analogical mechanism, we have found that the main differences between near and far transfer involve the amount of generalization that must be performed prior to transfer and the way that the matching process treats nonidentical predicates. We present here two extensions of our analogical matcher, minimal ascension and metamapping, that enable far transfer between representations with different relational vocabulary. Evidence for the effectiveness of these techniques is provided by a large-scale external evaluation, involving a substantial number of novel distant analogs.

Understanding conceptual change is an important problem in modeling human cognition and in making integrated AI systems that can learn autonomously. This paper describes a model of explanation-based conceptual change, integrating sketch understanding, analogical processing, qualitative models, truth-maintenance, and heuristic-based reasoning within the Companions cognitive architecture. Sketch understanding is used to automatically encode stimuli in the form of comic strips. Qualitative models and conceptual quantities are constructed for new phenomena via analogical reasoning and heuristics. Truth-maintenance is used to integrate conceptual and episodic knowledge into explanations, and heuristics are used to modify existing conceptual knowledge in order to produce better explanations. We simulate the learning and revision of the concept of force, testing the concepts learned via a questionnaire of sketches given to students, showing that our model follows a similar learning trajectory.

Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.

Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.

We are developing Companion Cognitive Systems, a new kind of software that can be effectively treated as a collaborator. Aside from their potential utility, we believe this effort is important because it focuses on three key problems that must be solved to achieve human-level AI: Robust reasoning and learning, interactivity, and longevity. We describe the ideas we are using to develop the first architecture for Companions: analogical processing, grounded in cognitive science for reasoning and learning, sketching and concept maps to improve interactivity, and a distributed agent architecture hosted on a cluster to achieve performance and longevity. We outline some results on learning by accumulating examples derived from our first experimental version.

The Eighteenth National Conference on Artificial Intelligence (AAAI-02) and the Fourteenth Conference on Innovative Applications of AI (IAAI- 02) were positively received by those who attended. This report provides a few snapshots of the vast and varied content of the 2002 conferences. Proceedings of AAAI-02 and IAAI-02 are available from AAAI Press (www.- aaaipress.org).

Analogy has a rich history in Western civilization. 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. The limitations of the modern view are illustrated by monster analogies, which show that analogy need not be regarded as something having a single form, format, or semantics. Analogy clearly does depend on the human ability to create and use well-defined or analytic formats for laying out propositions that express or imply meanings and perceptions. 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. I argue that future efforts could be aimed at integration. This aim would include the incorporation of contextual information, the construction of semantic bases that are dynamic and knowledge rich, and the incorporation of multiple approaches to the problems of inference constraint.