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Franz J Kurfess
Cal Poly SLO
San Luis Obispo, California

Franz J. Kurfess joined the Computer Science Department of California Polytechnic State University in the summer of 2000, after a short stay with Concordia University in Montreal, Canada, and a longer stay with the New Jersey Institute of Technology. Before that, he spent some time with the University of Ulm, Germany, as a postdoc at the International Computer Science Institute in Berkeley, CA, and at the Technical University in Munich, where he obtained his MS and PhD in Computer Science.

At Cal Poly, he is the coordinator of the human-computer interaction lab, and teaches courses in the areas of artificial intelligence, knowledge-based systems, user-centered design and development, and human-computer interaction. His main areas of research are artificial intelligence and human-computer interaction, with particular interest in the usability and interaction aspects of knowledge-intensive systems. He is currently investigating a framework for the analysis of “interaction spaces,” consisting of the physical space where interaction between humans and computational systems takes place, and a conceptual space delineated between the shared communication channels, symbol systems, vocabularies and languages, and the conceptual model of the domain and the world. So far, humans have been able to accommodate the limitations of computational systems concerning such interactions fairly well. When expanding interaction to situations where robots (or computational systems in general) have to communicate with other robots, it becomes much more critical to have a coherent framework for interaction in place.


A scalable preference model for autonomous decision-making
Peters M., Saar-Tsechansky M., Ketter W., Williamson S., Groot P., Heskes T.  Machine Learning 107(6): 1039-1068, 2018. Type: Article

In some consumer markets, prices are determined by the limited availability of goods and customers choose from a small set of options. The outcome is often determined by simple tradeoffs between the most critical attributes....


Semantic-aware top-k spatial keyword queries
Qian Z., Xu J., Zheng K., Zhao P., Zhou X.  World Wide Web 21(3): 573-594, 2018. Type: Article

The authors present their enhancements to spatial keyword queries by using probabilistic topic modeling to incorporate semantic information. The topic model is based on latent Dirichlet allocation (LDA), which performs a statistical analysis to de...


Change-aware community detection approach for dynamic social networks
Samie M., Hamzeh A.  Applied Intelligence 48(1): 78-96, 2018. Type: Article

On a walk in the hills above our house the other day, I watched two flocks of birds merge, fly around as one flock for a few minutes, and then separate into two again. While I assume that the merging and unmerging started and ended with roughly th...


Answer set programming for non-stationary Markov decision processes
Ferreira L., Bianchi R., Santos P., Lopez de Mantaras R.  Applied Intelligence 47(4): 993-1007, 2017. Type: Article

Problem solving with computers often involves the exploration of paths from an initial state to a goal state. In addition to the size of this search space, there are many factors complicating this approach, especially in realistic environments. In...


D-Brane: a diplomacy playing agent for automated negotiations research
de Jonge D., Sierra C.  Applied Intelligence 47(1): 158-177, 2017. Type: Article

Diplomacy is a board game conceived in the 1950s, set in Europe before World War I. Two to seven players control the armed forces of major European powers, establishing and betraying alliances through negotiation and movement actions. Among...


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