Human–Machine Collaboration for Democratizing Data Science

Everybody wants to analyse their data, but only few posses the data science expertise to do this. Motivated by this observation, we introduce a novel framework and system VisualSynth for human-machine collaboration in data science. Its aim is to democratize data science by allowing users to interact with standard spreadsheet software in order to perform and automate various data analysis tasks ranging from data wrangling, data selection, clustering, constraint learning, predictive modeling and auto-completion. VisualSynth relies on the user providing colored sketches, i.e., coloring parts of the spreadsheet, to partially specify data science tasks, which are then determined and executed using artificial intelligence techniques.

Managing caching strategies for stream reasoning with reinforcement learning

Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is quite limited and, e.g., they cannot be used to encode problems with constraints, which often appear in practice. In this paper, we suggest a novel approach that uses the Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy solutions by using intelligent management of learned constraints. In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one. Evaluations conducted on real-world reconfiguration problems show that providing a CDCL algorithm with relevant learned constraints from previous iterations results in significant performance improvements of the algorithm in stream reasoning scenarios.

On Irrelevant Literals in Pseudo-Boolean Constraint Learning

Learning pseudo-Boolean (PB) constraints in PB solvers exploiting cutting planes based inference is not as well understood as clause learning in conflict-driven clause learning solvers. In this paper, we show that PB constraints derived using cutting planes may contain irrelevant literals, i.e., literals whose assigned values (whatever they are) never change the truth value of the constraint. Such literals may lead to infer constraints that are weaker than they should be, impacting the size of the proof built by the solver, and thus also affecting its performance. This suggests that current implementations of PB solvers based on cutting planes should be reconsidered to prevent the generation of irrelevant literals. Indeed, detecting and removing irrelevant literals is too expensive in practice to be considered as an option (the associated problem is NP-hard).

Rapid phonotactic constraint learning in aging: Evidence from speech errors

Older adults are able to implicitly pick up structural regularities in the environment in a relatively unaffected way despite age-related cognitive decline. Although there is extensive evidence for this observation in the domain of motor skill learning, it is not clear whether this is also true for aspects of language learning. In this study, we investigate the effect of aging on implicitly learning novel phonotactic constraints in the native spoken language. During four sessions on consecutive days, a group of fifteen young (18-25 years) and fifteen healthy older (74-82 years) Dutch-speaking adults were asked to rapidly recite sequences of syllables conform Dutch phonotactics (e.g., siet mieng kief hien). Within the setting of the experiment, two unrestricted consonants in the Dutch spoken language were constrained to an onset or coda position depending on the medial vowel. Analysis of speech errors revealed rapid adherence to the novel second- order constraints in the older group. Strikingly, the effect mirrors earlier developmental work with children using the same paradigm (Smalle, Muylle, Szmalec, & Duyck, 2017). The findings are discussed in light of possible age-dependent differences in implicit and explicit cognitive subsystems underlying human skill learning.