Visualizing Inference

Graphical visualization has demonstrated enormous power in helping people to understand complexity in many branches of science. But, curiously, AI has been slow to pick up on the power of visualization. Alar is a visualization system intended to help people understand and control symbolic inference. Alar presents dynamically controllable node-and-arc graphs of concepts, and of assertions both supplied to the system and inferred. Alar is useful in quality assurance of knowledge bases (finding false, vague, or misleading statements; or missing assertions). It is also useful in tuning parameters of inference, especially how "liberal vs. conservative" the inference is (trading off the desire to maximize the power of inference versus the risk of making incorrect inferences). We present a typical scenario of using Alar to debug a knowledge base.

[1]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[2]  Stephen Travis Pope,et al.  A cookbook for using the model-view controller user interface paradigm in Smalltalk-80 , 1988 .

[3]  Akrivi Katifori,et al.  Ontology visualization methods—a survey , 2007, CSUR.

[4]  Desney S. Tan,et al.  EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers , 2009, CHI.

[5]  James Pustejovsky,et al.  Coarse Word-Sense Disambiguation Using Common Sense , 2010, AAAI Fall Symposium: Commonsense Knowledge.

[6]  Marc Eisenstadt,et al.  The transparent Prolog machine - visualizing logic programs , 1991 .

[7]  LepourasGeorge,et al.  Ontology visualization methodsa survey , 2007 .

[8]  Desney S. Tan,et al.  Effective End-User Interaction with Machine Learning , 2011, AAAI.

[9]  Henry Lieberman,et al.  AnalogySpace: Reducing the Dimensionality of Common Sense Knowledge , 2008, AAAI.

[10]  Henry Lieberman,et al.  Finding your way in a multi-dimensional semantic space with luminoso , 2010, IUI '10.

[11]  Ole J. Mengshoel,et al.  Visualizing and Understanding Large-Scale Bayesian Networks , 2011, Scalable Integration of Analytics and Visualization.

[12]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[13]  Andreas Wierse,et al.  Information Visualization in Data Mining and Knowledge Discovery , 2001 .

[14]  Henry Lieberman,et al.  Digital Intuition: Applying Common Sense Using Dimensionality Reduction , 2009, IEEE Intelligent Systems.