Important aspects of human cognition, like creativity and play, involve dealing with multiple divergent views of objects, goals, and plans. We argue in this paper that the current model of optimization that drives much of modern machine learning research is far too restrictive a paradigm to mathematically model the richness of human cognition. Instead, we propose a much more flexible and powerful framework of equilibration, which not only generalizes optimization, but also captures a rich variety of other problems, from game theory, complementarity problems, network equilibrium problems in economics, and equation solving. Our thesis is that creative activity involves dealing not with a single objective function, which optimization requires, but rather balancing multiple divergent and possibly contradictory goals. Such modes of cognition are better modeled using the framework of variational inequalities (VIs). We provide a brief review of this paradigm for readers unfamiliar with the underlying mathematics, and sketch out how VIs can account for creativity and play in human and animal cognition.

In this paper we propose a novel DCOP algorithm, called DJAO, that is able toefficiently find a solution with low communication overhead; this algorithm can be used for optimal and bounded approximate solutions by appropriately setting the error bounds. Our approach builds on distributed junction trees used in Action-GDL to represent independence relationsamong variables. We construct an AND/OR search space based on these junction trees.This new type of search space results in higher degrees for each OR node, consequently yielding a more efficient search graph in the distributed settings. DJAO uses a branch-and-bound search algorithm to distributedly find solutions within this search graph. We introduce heuristics to compute the upper and lower boundestimates that the search starts with, which is integral to our approach for reducing communication overhead. We empirically evaluate our approach in various settings.

Abstracts of the invited talks presented at the AAAI Fall Symposium on Discovery Informatics: AI Takes a Science-Centered View on Big Data. Talks include  A Data Lifecycle Approach to Discovery Informatics,  Generating Biomedical Hypotheses Using Semantic Web Technologies,  Socially Intelligent Science, Representing and Reasoning with Experimental and Quasi-Experimental Designs, Bioinformatics Computation of Metabolic Models from Sequenced Genomes, Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science,  Predictive Modeling of Patient State and Therapy Optimization, Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos,  Computational Analysis of Complex Human Disorders, and Look at This Gem: Automated Data Prioritization for Scientific Discovery of Exoplanets, Mineral Deposits, and More.

We present a graph-theoretic model of discourse based on the Questions Under Discussion (QUD) framework. Questions and assertions are treated as edges connecting discourse states in a rooted graph, modeling the introduction and resolution of various QUDs as paths through this graph. The amount of common ground presupposed by interlocutors at any given point in a discourse corresponds to graphical depth. We introduce a new task-oriented dialogue corpus and show that experts, presuming a richer common ground, initiate discourse at a deeper level than novices. The QUD-graph model thus enables us to quantify the experthood of a speaker relative to a fixed domain and to characterize the ways in which rich common ground facilitates more efficient communication.