1. Ergun Biçici. Prolegomenon to Commonsense Reasoning in User Interfaces. ACM Crossroads, 9(1), 2002. [WWW] [PDF] Keyword(s): Artificial Intelligence. Abstract:

   Human-computer interaction experiences unbalanced talents of counterparts in the user interface, which can only be eased with the introduction of new solutions. Commonsense reasoning is a promising answer that offers formalization and computational models about how humans reason and think in a sensible way. In user interface design, assumed conventions and rules are widely followed and carried to user interfaces. For the most part, these assumptions are obvious to humans yet incomprehensible to computers. As a result, it is essential that tools are developed, capable of retrieving relevant, sensible inferences that in turn can serve as catalysts for future reasoning. Embedding this tool in user interfaces can provide many benefits including representation of assumptions and unspoken rules, the addition of useful tool abilities, and increasingly usable and accessible environments where computers and humans have extended communication capabilities to assist in understanding each other. In this article, we present groundwork for applying commonsense reasoning to user interfaces. We start with identifying the asymmetry in human-machine communication and later focus on some approaches such as softbots and the proposed anti-mac interface. Then we identify the problems faced in user interfaces such as the correspondence problem inherited from computer vision. Next, we state our research ambition as adding commonsense reasoning functionality to user interfaces and further survey previous approaches and the state of the art. We depict the big picture we are facing and list some of the rewards to be earned by applying this technique. Later, characteristics of common sense are investigated together with examples in first-order logic. We then report various lessons learned from earlier attempts that began in physical systems concentrated on small microworld problems. Finally, we cite a sample methodology for automating commonsense reasoning and identify a number of questions that have yet to be answered.

   
   

   @article{ProlegomenonCSRUI,
   author = {Ergun Bi\c{c}ici},
   title = {Prolegomenon to Commonsense Reasoning in User Interfaces},
   journal = {ACM Crossroads},
   volume = {9},
   number = {1},
   series = {Human Computer Interaction},
   year = {2002},
   keywords = {Artificial Intelligence},
   url = {http://bicici.github.io/publications/2002/ACMC/CommonsenseUI6.htm},
   pdf = {http://bicici.github.io/publications/2002/ACMC/Prolegomenon2.pdf},
   abstract = {Human-computer interaction experiences unbalanced talents of counterparts in the user interface, which can only be eased with the introduction of new solutions. Commonsense reasoning is a promising answer that offers formalization and computational models about how humans reason and think in a sensible way. In user interface design, assumed conventions and rules are widely followed and carried to user interfaces. For the most part, these assumptions are obvious to humans yet incomprehensible to computers. As a result, it is essential that tools are developed, capable of retrieving relevant, sensible inferences that in turn can serve as catalysts for future reasoning. Embedding this tool in user interfaces can provide many benefits including representation of assumptions and unspoken rules, the addition of useful tool abilities, and increasingly usable and accessible environments where computers and humans have extended communication capabilities to assist in understanding each other. In this article, we present groundwork for applying commonsense reasoning to user interfaces. We start with identifying the asymmetry in human-machine communication and later focus on some approaches such as softbots and the proposed anti-mac interface. Then we identify the problems faced in user interfaces such as the correspondence problem inherited from computer vision. Next, we state our research ambition as adding commonsense reasoning functionality to user interfaces and further survey previous approaches and the state of the art. We depict the big picture we are facing and list some of the rewards to be earned by applying this technique. Later, characteristics of common sense are investigated together with examples in first-order logic. We then report various lessons learned from earlier attempts that began in physical systems concentrated on small microworld problems. Finally, we cite a sample methodology for automating commonsense reasoning and identify a number of questions that have yet to be answered.},
   
   }
   

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