Inhibition and young children's performance on the Tower of London task

Young children, when performing problem solving tasks, show a tendency to break task rules and produce incomplete solutions. We propose that this tendency can be explained by understanding problem solving within the context of the development of “executive functions” – general cognitive control functions, which serve to regulate the operation of the cognitive system. This proposal is supported by the construction of two computational models that simulate separately the performance of 3–4 year old and 5–6 year old children on the Tower of London planning task. We seek in particular to capture the emerging role of inhibition in the older group. The basic framework within which the models are developed is derived from Fox and Das’ Domino model [Fox, J., & Das, S. (2000). Safe and sound: Artificial intelligence in hazardous applications. Cambridge, MA: MIT Press] and Norman and Shallice’s [Norman, D.A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behaviour. In R. Davidson, G. Schwartz, & D. Shapiro (Eds.), Consciousness and Self Regulation (Vol. 4). New York: Plenum] theory of willed and automatic action. Two strategies and a simple perceptual bias are implemented within the models and comparisons between model and child performance reveal a good fit for the key dependent measures (number of rule breaks and percentage of incomplete solutions) of the two groups

Diameters, distortion and eigenvalues

We study the relation between the diameter, the first positive eigenvalue of the discrete $p$-Laplacian and the $\ell_p$-distortion of a finite graph. We prove an inequality relating these three quantities and apply it to families of Cayley and Schreier graphs. We also show that the $\ell_p$-distortion of Pascal graphs, approximating the Sierpinski gasket, is bounded, which allows to obtain estimates for the convergence to zero of the spectral gap as an application of the main result.Comment: Final version, to appear in the European Journal of Combinatoric

Circuit of Computer Science Unplugged activities based on the life of Ada Lovelace

Ada Lovelace’s life is a source of inspiration for women and men of all ages, for being a bright-minded and visionary person. His greatest achievement was to have written the first computer program in history. This article presents a methodological proposal for didactic use of a children’s book about Ada Lovelace by proposing a circuit of unplugged activities in order to refine the computational thinking (CT) in children and adolescents. This methodology was applied in a workshop during the BLIND in the BLIND Conference. A quali-quantitative analysis performed with participants indicates the suitability of the proposed methodology

Boosting Multi-Core Reachability Performance with Shared Hash Tables

This paper focuses on data structures for multi-core reachability, which is a key component in model checking algorithms and other verification methods. A cornerstone of an efficient solution is the storage of visited states. In related work, static partitioning of the state space was combined with thread-local storage and resulted in reasonable speedups, but left open whether improvements are possible. In this paper, we present a scaling solution for shared state storage which is based on a lockless hash table implementation. The solution is specifically designed for the cache architecture of modern CPUs. Because model checking algorithms impose loose requirements on the hash table operations, their design can be streamlined substantially compared to related work on lockless hash tables. Still, an implementation of the hash table presented here has dozens of sensitive performance parameters (bucket size, cache line size, data layout, probing sequence, etc.). We analyzed their impact and compared the resulting speedups with related tools. Our implementation outperforms two state-of-the-art multi-core model checkers (SPIN and DiVinE) by a substantial margin, while placing fewer constraints on the load balancing and search algorithms.Comment: preliminary repor

Set-shifting as a component process of goal-directed problem-solving

In two experiments, we compared secondary task interference on Tower of London performance resulting from three different secondary tasks. The secondary tasks were designed to tap three different executive functions, namely set-shifting, memory monitoring and updating, and response inhibition. Previous work using individual differences methodology suggests that, all other things being equal, the response inhibition or memory tasks should result in the greatest interference. However, this was not found to be the case. Rather, in both experiments the set-shifting task resulted in significantly more interference on Tower of London performance than either of the other secondary tasks. Subsequent analyses suggest that the degree of interference could not be attributed to differences in secondary task difficulty. Results are interpreted in the light of related work which suggests that solving problems with non-transparent goal/subgoal structure requires flexible shifting between subgoals – a process that is held to be impaired by concurrent performance of a set-shifting task

Analysis of Generalized Artificial Intelligence Potential through Reinforcement and Deep Reinforcement Learning Approaches

Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. Deep reinforcement learning agents were observed to handle a wider range of problems, but behave inferior to specialized reinforcement learning algorithms

Improving the programming language translation process via static structure abstraction and algorithmic code transliteration

Fully automated programming language translation has been described as an unrealistic goal, with previous research being limited by a ceiling of 90% successful code translation. The key issues hindering automatic translation efficacy are the: maintainability of the translated constructs; full utilisation of the target language\u27s features; and amount of manual intervention required to complete the translation process. This study has concentrated on demonstrating improvements to the translation process by introducing the programming-language-independent, Unified Modelling Language (UML) and Computer Assisted Software Engineering (CASE) tools to the legacy-system language migration project. UML and CASE tools may be used to abstract the static framework of the source application to reduce the so called opaqueness of the translated constructs, yielding a significantly more maintainable product. The UML and CASE tools also enhance use of the target language features, through forward engineering of the native constructs of the target language during the reproduction of the static framework. Source application algorithmic code translation, performed as a separate process using transliteration, may preserve maximum functionality of the source application after completion of the static structure translation process. Introduction of the UML and CASE tools in conjunction with algorithmic code transliteration offers a reduction of the manual intervention required to complete the translation process