The Reciprocal Influences among Motivation,Personality Traits,and Game Habits for Playing Pokémon GO

This article reports a study exploring motivations of Pokémon Game use, individual differences related to personality traits, and game habits. First, it analyzed Pokémon GO motivations through exploratory factor analysis (EFA) by administering online the Pokémon GO Motivational Scale to a group of Italian gamers (N = 560). Successively, a Confirmatory Factor Analysis (CFA) was conducted testing three factorial models of Pokémon Game motivations on a selected random sample (N = 310). Results showed a three-factor model of Pokémon GO Game motivations (i.e. Personal Needs, Social Needs and Recreation), accounting for 68.9% of total variance plus a general higher order factor that best fits the data. Individual differences in Pokémon GO motivations and personality traits have been explored showing that high involved Pokémon GO players are introverted, low agreeableness, and conscientiousness people, driven by personal social and recreational needs. Reciprocal influences on motivational involvement, personality, and game habits were discussed.     

On the evolution of homogeneous two-robot teams: clonal versus aclonal approaches

This study compares two different evolutionary approaches (clonal and aclonal) to the design of homogeneous two-robot teams (i.e. teams of morphologically identical agents with identical controllers) in a task that requires the agents to specialise to different roles. The two approaches differ mainly in the way teams are formed during evolution. In the clonal approach, a team is formed from a single genotype within one population of genotypes. In the aclonal approach, a team is formed from multiple genotypes within one population of genotypes. In both cases, the goal is the synthesis of individual generalist controllers capable of integrating role execution and role allocation mechanisms for a team of homogeneous robots. Our results diverge from those illustrated in a similar comparative study, which supports the superiority of the aclonal versus the clonal approach. We question this result and its theoretical underpinning, and we bring new empirical evidence showing that the clonal outperforms the aclonal approach in generating homogeneous teams required to dynamically specialise for the benefit of the team. The results of our study suggest that task-specific elements influence the evolutionary dynamics more than the genetic relatedness of the team members. We conclude that the appropriateness of the clonal approach for role allocation scenarios is mainly determined by the specificity of the collective task, including the evaluation function, rather than by the way in which the solutions are evaluated during evolution.     

Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning

The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover. It has been adopted to the setting of evolutionary algorithms and, a lot more recently, reinforcement learning and Monte-Carlo tree search methodology to cope with a rather challenging question of action evaluation at the chance nodes. The theorem motivates novel dynamic parallel algorithms that are explicitly described in the current paper for the first time. The algorithms involve independent agents traversing a dynamically constructed directed graph that possibly has loops and multiple edges. A rather elegant and profound category-theoretic model of cognition in biological neural networks developed by a well-known French mathematician, professor Andree Ehresmann jointly with a neurosurgeon, Jan Paul Vanbremeersch over the last thirty years provides a hint at the connection between such algorithms and Hebbian learning.     

Combining stochastic programming and optimal control to decompose multistage stochastic optimization problems

The paper suggests a possible cooperation between stochastic programming and optimal control for the solution of multistage stochastic optimization problems. We propose a decomposition approach for a class of multistage stochastic programming problems in arborescent form (i.e. formulated with implicit non-anticipativity constraints on a scenario tree). The objective function of the problem can be either linear or nonlinear, while we require that the constraints are linear and involve only variables from two adjacent periods (current and lag 1). The approach is built on the following steps. First, reformulate the stochastic programming problem into an optimal control one. Second, apply a discrete version of Pontryagin maximum principle to obtain optimality conditions. Third, discuss and rearrange these conditions to obtain a decomposition that acts both at a time stage level and at a nodal level. To obtain the solution of the original problem we aggregate the solutions of subproblems through an enhanced mean valued fixed point iterative scheme.     

On the second-order sliding mode control of nonlinear systems with uncertain control direction

This note deals with the implementation of a second-order sliding mode control algorithm for a class of nonlinear systems in which the sign of the high-frequency gain, though constant, is unknown. A specific second-order sliding mode control algorithm, the “Suboptimal” algorithm, is properly modified in order to face the uncertainty in the control direction. It is shown that after a finite time the uncertain sign is identified and the standard finite time convergence takes place from that time on. Simulation results are provided.     

Contact force regulation in wire-actuated pantographs via variable structure control and frequency-domain techniques

One of the main problems in high-speed-train transportation systems is related to the current collection quality, that can dramatically decrease because of oscillations of the pantograph-catenary system. This problem has been addressed by means of active pantographs. In this paper we present some results about the possible implementation of variable structure control (VSC) techniques on a wire actuated symmetric pantograph. Such an actuator was suggested in the literature as a viable solution to building an active pantograph by modifying a passive pantograph currently used by Italian Railways. The use of VSC with sliding modes was considered in order to cope with the system uncertainties due to the overhead suspended catenary. Recent results about the frequency-based analysis of VSC systems featuring second-order sliding modes are exploited to avoid the performance-destroying effect of the resonant wire actuator and to get a continuous control force without using observers. We show by simulations that the contact force results are very close to the desired set-point also in the presence of measurement noise.     

From Böhm's Theorem to Observational Equivalences: an Informal Account

There are essentially two ways of looking at the computational behaviours of λ-terms. One consists in putting the term within a context (possibly of λ-calculus extensions) and observing some properties (typically termination). The other consists in reducing the term until some meaningful information is obtained: this naturally leads to a tree representation of the information implicitly contained in the original term. The paper is an informal overview of the role played by Böhm's Theorem in these observations of terms.