This paper offers a perspective on Symbiotic Cognitive Systems that draws on Distributed Cognition. It argues that representations are the medium of cognition, and that the external representations that are one of the foci of Distributed Cognition are critical to supporting symbiosis. The paper analyzes an instance of a symbiotic cognitive system in which hundreds of human participants – with the support of a digital system – collectively optimize a program. It discusses the roles external representations play in symbiosis, and suggest that the design of external representations that are accessible and legible to both human and digital agents is a critical part of symbiotic cognitive systems.

Analytic systems provide insights to decision makers based on data. Conclusion quality depends on the input data and the reasoning steps made by analysts during exploration. This paper presents CATS, a system able to: (i) Store and leverage information about analytical processes conducted by analysts; (ii) Improve the quality and confidence on analytical reports, by improving how well analysts recall their activities; (iii) Provide ways of comparing different analytical reports; (iv) Help with the dissemination of best practices within analytical teams; (v) Produce the provenance of analytical reports. The use of CATS is illustrated through an example of tracking the work in WISE (Weather InSights Environment) a real tool that combines forecast and observed environmental data to provide an integrated platform to make informed decisions.

Model-Based Diagnosis (MBD) is a principled and domain-independent way of analyzing why a system under examination is not behaving as expected. Given an abstract description (model) of the system's components and their behavior when functioning normally, MBD techniques rely on observations about the actual system behavior to reason about possible causes when there are discrepancies between the expected and observed behavior. Due to its generality, MBD has been successfully applied in a variety of application domains over the last decades. In many application domains of MBD, testing different hypotheses about the reasons for a failure can be computationally costly, e.g., because complex simulations of the system behavior have to be performed. In this work, we therefore propose different schemes of parallelizing the diagnostic reasoning process in order to better exploit the capabilities of modern multi-core computers. We propose and systematically evaluate parallelization schemes for Reiter's hitting set algorithm for finding all or a few leading minimal diagnoses using two different conflict detection techniques. Furthermore, we perform initial experiments for a basic depth-first search strategy to assess the potential of parallelization when searching for one single diagnosis. Finally, we test the effects of parallelizing "direct encodings" of the diagnosis problem in a constraint solver.

The paper focuses on finding the m best solutions to combinatorial optimization problems using best-first or depth-first branch and bound search. Specifically, we present a new algorithm m-A*, extending the well-known A* to the m-best task, and for the first time prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since best-first algorithms require extensive memory, we also extend the memory-efficient depth-first branch and bound to the m-best task. We adapt both algorithms to optimization tasks over graphical models (e.g., Weighted CSP and MPE in Bayesian networks), provide complexity analysis and an empirical evaluation. Our experiments confirm theory that the best-first approach is largely superior when memory is available, but depth-first branch and bound is more robust. We also show that our algorithms are competitive with related schemes recently developed for the m-best task.

This paper describes R3 (Reading, Reasoning, and Reporting), our system for deep language understanding and model management for the biomedical domain. Starting from a base BioPAX model, we learn extensions to it by reading biomedical research articles from PubMed Central. We describe the particular issues for text understanding in this domain and how we use pre- and post-analysis reasoning to bridge the differences in how knowledge is packaged in a text and in a biomedical database. We close with brief description of our first year results, where R3 was faster than all other reported systems, reading 1,000 articles in 15 minutes.

Model counting and weighted model counting are key problems in artificial intelligence. Marginal inference can be reduced to model counting in many statistical-relational systems, such as Markov Logic. One common approach used by model counters is splitting a theory into disjoint subtheories, performing model counting on the subtheories, and then caching the result. If an identical subtheory is encountered again in the search, the cached result is used, greatly reducing runtime. In this work we introduce a way to cache symmetric subtheories compactly, which could potentially decrease required cache size, increase cache hits, and decrease runtime of solving.

Artificial Intelligence (AI) has four seasons: hype, disappointment, funding drought, and renewed interest. I've been involved in AI research for quite some time -- I became a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 1993 -- and I've weathered several seasonal cycles. What I'm seeing now, however, is the most puzzling cycle yet; either I'm getting old and addled, or the current cycle is unique in its magnitude. In these Big Data days, the big talk about AI's potential reminds me of what happened at the peak of earlier cycles (see, for example, the recent Wall Street Journal article. Once again, the focus is on a single technical component -- deep learning -- and hopes seem to be building that it can solve many very hard problems easily and more or less magically.

A similar arguments can be used for graphs (as in graph theory), non-binary trees, heaps, stacks, linked lists etc. Indeed, before programming languages offered advanced data structures, sophisticated objects and types, recursion (and much more) -- all the data -- had to be stored in (organized) arrays. Trees, hash tables etc. were simulated by using arrays and pointers. In some ways, we are getting back to the old times, with unstructured data, such as member postings on social networks. Although structuring unstructured data (by putting it into clusters and taxonomies) allow it to be manipulated much more easily.

It's been more than 20 years since IBM's Deep Blue won its first match against world chess champion Garry Kasparov, marking the first time an artificial intelligence machine defeated a reigning champion. Deep Blue eventually lost the match 2-4, but evened the score in a May 1997 rematch. Fourteen years later, AI made its television debut in grand style, when IBM's Watson took down a pair of former "Jeopardy!" In milliseconds, the machine culled the most probable answer to each question from more than 200 million pages of content, including the complete Wikipedia catalog. Now, Google's AI system, AlphaGo, is making cognitive computing history.

Recently, MaxSAT reasoning is shown very effective in computing a tight upper bound for a Maximum Clique (MC) of a (unweighted) graph. In this paper, we apply MaxSAT reasoning to compute a tight upper bound for a Maximum Weight Clique (MWC) of a wighted graph. We first study three usual encodings of MWC into weighted partial MaxSAT dealing with hard clauses, which must be satisfied in all solutions, and soft clauses, which are weighted and can be falsified. The drawbacks of these encodings motivate us to propose an encoding of MWC into a special weighted partial MaxSAT formalism, called LW (Literal-Weighted) encoding and dedicated for upper bounding an MWC, in which both soft clauses and literals in soft clauses are weighted. An optimal solution of the LW MaxSAT instance gives an upper bound for an MWC, instead of an optimal solution for MWC. We then introduce two notions called the Top-k literal failed clause and the Top-k empty clause to extend classical MaxSAT reasoning techniques, as well as two sound transformation rules to transform an LW MaxSAT instance. Successive transformations of an LW MaxSAT instance driven by MaxSAT reasoning give a tight upper bound for the encoded MWC. The approach is implemented in a branch-and-bound algorithm called MWCLQ. Experimental evaluations on the broadly used DIMACS benchmark, BHOSLIB benchmark, random graphs and the benchmark from the winner determination problem show that our approach allows MWCLQ to reduce the search space significantly and to solve MWC instances effectively. Consequently, MWCLQ outperforms state-of-the-art exact algorithms on the vast majority of instances. Moreover, it is surprisingly effective in solving hard and dense instances.