A critical challenge in the creation of Non-Playable Characters (NPCs) lies in the alignment between personality traits and emotions with the accurate portrayal of facial expressions. A mismatch between these two character’s aspects or the absence of effective synchronization between them could damage the believability of the game, with subsequent negative impacts on the player’s level of immersion and experience. This research introduces FACial EXpressions (FAC.EX), a software module crafted to enhance video game development by endowing NPCs with nuanced facial and internal responses during dialogues. Insights collected from feedback provided by 20 volunteers, including gamers and developers, affirm the perspective effectiveness of the module in simulating human-like facial expressions and emotions. The obtained positive results lay the foundation for potential future extensions aimed at meeting the dynamic requirements of the evolving game industry.

Recently, designers of the home automation framework are looking to the Internet of Things (IoT) model to make it easier to deploy devices capable of gathering data from various plants. This situation is pushing a further evolution: from the conventional building automation, where the logic is determined by the installer or by the customer, to a cognitive system, capable of learning from the customers' preferences what the best plant configuration would be. The aim of this research work is to identify issues related to the implementation of cognitive solutions during the renovation process of buildings. The paper proposes a cognitive framework to help the operation and management phases of buildings, by means of the digitalization of the supply chain of the construction industry, from the single building aspect to the entire construction cycle. The paper presents the design of smart building elements with integrated capability to generate a massive amount of data for monitoring the energy and maintenance status of renovated buildings. The IoT paradigm has been used to link the physical word of building elements with the cognitive layer. The proposed approach has been applied and validated into a real test case.

In this paper, aHinfincontrol technique is proposed for the management of a supply chain (SC) model linearized about nominal operating conditions. The performance considered is a weightedHinfinnorm comprising the inventory and the orders placed by each site, hence controlling the bullwhip effect together with local management costs. It is shown how the optimization of local costs at each site is related to the performance of the whole chain and a decentralized control methodology is proposed based on this relation.

This paper introduces a new analytical algorithm to perform the localization of a mobile robot using odometry and laser readings. Based on a polynomial approach, the proposed algorithm provides the optimal affine filter in a class of suitably defined estimators. The performance of the algorithm has been evaluated through simulations. The comparison with the standard Extended Kalman Filter shows that the proposed filter provides good estimates also in critical situations where the system nonlinearities cause a bad behavior of the EKF.

A new algorithm for dynamic routing of LSPs is proposed. While off-line algorithms are not suitable due to the necessary a priori knowledge of future LSP setup requests, our proposed algorithm, SPeCRA (stochastic performance comparison routing algorithm), does not assume any specific stochastic traffic model and does not require any knowledge of future demands. Both features are a must for the new Internet traffic. In order to analyze SPeCRA's performance, we compare the ISP rejection ratios of SPeCRA and MIRA (Kar, K. et al., IEEE J. on Selected Areas in Commun., vol.18, no.12, p.2566-79, 2000). SPeCRA is easy to implement. It can be implemented using only simple shortest-hop or shortest-cost algorithms, which are interesting solutions for vendors, and not as computationally heavy as other routing algorithms.

A finite buffer, single part-type, single unreliable machine production system is considered. The part flow into the system is described through a fluid model. The problem is to determine a production control which minimizes an infinite horizon average back-log/surplus cost. In the infinite buffer capacity case, this problem is solved by a hedging point policy, and the hedging point can be analytically computed. In this paper we consider the finite capacity case, derive an equation to compute the optimal hedging point and solve it numerically. A comparison between the optimal hedging point in the finite buffer capacity case and the optimal hedging point in the infinite buffer capacity case is reported in the paper, together with the analysis of the effect of some meaningful parameters (like the buffer capacity and the demand loss cost) on the computed hedging point. Some numerical examples illustrate the results presented in the paper.

Rigid formations of mobile robots are to be used for special missions in which the task-execution requires a tight cooperation of all units in the group so as to constrain them to keep preassigned mutual distances. In the paper an algorithm for the optimal path-planning of rigid formations of mobile robots is considered for a case in which the path cost index is given by the sum of all distances covered by the robots in the group. The proposed solution method provides an approximate solution to the problem based on a discretization of the configuration space of the formation. A dynamic programming algorithm is used then to find the optimal path in configuration space. Several examples are introduced to show the effectiveness of the proposed dynamic programming method as compared to three heuristic strategies which are also devised in the paper.

We study the problem of dynamic part scheduling and kanban allocation on a pull manufacturing system, with finite inventory space and stochastic demand. It is assumed that a finite number of requests can be backlogged and requests finding the full system are dropped. The system is a single machine, two part-type, and the control objective is that of minimizing an infinite horizon discounted cost in the inventory and backlog level and demand losses. No setup times and costs are considered. The original scheduling and kanban allocation problem is reformulated as a "pure" scheduling problem for constant kanban systems. A continuous-time Markov decision problem is formulated, and solved by means of a uniformization procedure and discrete-time dynamic programming. Some initial analytical results are given, while the solution structure is derived numerically.

The problem of dynamic control of manufacturing systems is addressed, and a solution is proposed in terms of a state reconstruction algorithm, that allows to exactly compute system performance under perturbed values of some system parameters. The control scheme is able to react to disturbances acting on the system. It is applied to a multi part-type manufacturing system, with the purpose of dynamically allocating buffer capacities in order to maximize system throughput. Simulation results are presented to illustrate the effectiveness of the scheme.