Forbus, Kenneth D.


Sketch Worksheets in STEM Classrooms: Two Deployments

AAAI Conferences

Sketching can be a valuable tool for science education, but it is currently underutilized. Sketch worksheets were developed to help change this, by using AI technology to give students immediate feedback and to give instructors assistance in grading. Sketch worksheets use visual representations automatically computed by CogSketch, which are combined with conceptual information from the OpenCyc ontology. Feedback is provided to students by comparing an instructor’s sketch to a student’s sketch, using the Structure-Mapping Engine. This paper describes our experiences in deploying sketch worksheets in two types of classes: Geoscience and AI. Sketch worksheets for introductory geoscience classes were developed by geoscientists at University of Wisconsin-Madison, authored using CogSketch and used in classes at both Wisconsin and Northwestern University. Sketch worksheets were also developed and deployed for a knowledge representation and reasoning course at Northwestern. Our experience indicates that sketch worksheets can provide helpful on-the-spot feedback to students, and significantly improve grading efficiency, to the point where sketching assignments can be more practical to use broadly in STEM education.



Towards a Comprehensive Standard Model of Human-Like Minds

AAAI Conferences

The Standard Model of particle physics has been an effective framework for describing entities of the domain relative to each other and thereby predicting missing particles. In this sense, it plays a similar role to the periodic table of the elements. Could something analogous work for AI? In this paper, we propse a dozen elements of a standard model of the mind and characterize their interrelationships. Naturally, this set reflects our own experience and biases and is not exhaustive. We also describe some of the ways in which this apporach to a standard model differs from that in physics and suggest some limitations.


Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

arXiv.org Artificial Intelligence

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.


Analogical Chaining with Natural Language Instruction for Commonsense Reasoning

AAAI Conferences

Understanding commonsense reasoning is one of the core challenges of AI. We are exploring an approach inspired by cognitive science, called analogical chaining, to create cognitive systems that can perform commonsense reasoning. Just as rules are chained in deductive systems, multiple analogies build upon each other’s inferences in analogical chaining. The cases used in analogical chaining – called common sense units – are small, to provide inferential focus and broader transfer. Importantly, such common sense units can be learned via natural language instruction, thereby increasing the ease of extending such systems. This paper describes analogical chaining, natural language instruction via microstories, and some subtleties that arise in controlling reasoning. The utility of this technique is demonstrated by performance of an implemented system on problems from the Choice of Plausible Alternatives test of commonsense causal reasoning.


Remembering Marvin Minsky

AI Magazine

Marvin Minsky, one of the pioneers of artificial intelligence and a renowned mathematicial and computer scientist, died on Sunday, 24 January 2016 of a cerebral hemmorhage. In this article, AI scientists Kenneth D. Forbus (Northwestern University), Benjamin Kuipers (University of Michigan), and Henry Lieberman (Massachusetts Institute of Technology) recall their interactions with Minksy and briefly recount the impact he had on their lives and their research. A remembrance of Marvin Minsky was held at the AAAI Spring Symposium at Stanford University on March 22. Video remembrances of Minsky by Danny Bobrow, Benjamin Kuipers, Ray Kurzweil, Richard Waldinger, and others can be on the sentient webpage1 or on youtube.com.


Remembering Marvin Minsky

AI Magazine

Marvin Minsky, one of the pioneers of artificial intelligence and a renowned mathematicial and computer scientist, died on Sunday, 24 January 2016 of a cerebral hemmorhage. He was 88. In this article, AI scientists Kenneth D. Forbus (Northwestern University), Benjamin Kuipers (University of Michigan), and Henry Lieberman (Massachusetts Institute of Technology) recall their interactions with Minksy and briefly recount the impact he had on their lives and their research. A remembrance of Marvin Minsky was held at the AAAI Spring Symposium at Stanford University on March 22. Video remembrances of Minsky by Danny Bobrow, Benjamin Kuipers, Ray Kurzweil, Richard Waldinger, and others can be on the sentient webpage1 or on youtube.com.


Software Social Organisms: Implications for Measuring AI Progress

AI Magazine

In this article I argue that achieving human-level AI is equivalent to learning how to create sufficiently smart software social organisms. This implies that no single test will be sufficient to measure progress. Instead, evaluations should be organized around showing increasing abilities to participate in our culture, as apprentices. This provides multiple dimensions within which progress can be measured, including how well different interaction modalities can be used, what range of domains can be tackled, what human-normed levels of knowledge they are able to acquire, as well as others. I begin by motivating the idea of software social organisms, drawing on ideas from other areas of cognitive science, and provide an analysis of the substrate capabilities that are needed in social organisms in terms closer to what is needed for computational modeling. Finally, the implications for evaluation are discussed.


Analogical Abduction and Prediction: Their Impact on Deception

AAAI Conferences

To deceive involves corrupting the predictions or explanations of others. A deeper understanding of how this works thus requires modeling how human abduction and prediction operate. This paper proposes that most human abduction and prediction are carried out via analogy, over experience and generalizations constructed from experience. I take experience to include cultural products, such as stories. How analogical reasoning and learning can be used to make predictions and explanations is outlined, along with both the advantages of this approach and the technical questions that it raises. Concrete examples involving deception and counter-deception are used to explore these ideas further.


Extending Analogical Generalization with Near-Misses

AAAI Conferences

Concept learning is a central problem for cognitive systems. Generalization techniques can help organize examples by their commonalities, but comparisons with non-examples, near-misses, can provide discrimination. Early work on near-misses required hand-selected examples by a teacher who understood the learner’s internal representations. This paper introduces Analogical Learning by Integrating Generalization and Near-misses (ALIGN) and describes three key advances. First, domain-general cognitive models of analogical processes are used to handle a wider range of examples. Second, ALIGN’s analogical generalization process constructs multiple probabilistic representations per concept via clustering, and hence can learn disjunctive concepts. Finally, ALIGN uses unsupervised analogical retrieval to find its own near-miss examples. We show that ALIGN out-performs analogical generalization on two perceptual data sets: (1) hand-drawn sketches; and (2) geospatial concepts from strategy-game maps.