Emotions as Abstract Evaluation Criteria in Biological and Artificial Intelligences

Biological as well as advanced artificial intelligences (AIs) need to decide which goals to pursue. We review nature's solution to the time allocation problem, which is based on a continuously readjusted categorical weighting mechanism we experience introspectively as emotions. One observes phylogenetically that the available number of emotional states increases hand in hand with the cognitive capabilities of animals and that raising levels of intelligence entail ever larger sets of behavioral options. Our ability to experience a multitude of potentially conflicting feelings is in this view not a leftover of a more primitive heritage, but a generic mechanism for attributing values to behavioral options that can not be specified at birth. In this view, emotions are essential for understanding the mind. For concreteness, we propose and discuss a framework which mimics emotions on a functional level. Based on time allocation via emotional stationarity (TAES), emotions are implemented as abstract criteria, such as satisfaction, challenge and boredom, which serve to evaluate activities that have been carried out. The resulting timeline of experienced emotions is compared with the “character” of the agent, which is defined in terms of a preferred distribution of emotional states. The long-term goal of the agent, to align experience with character, is achieved by optimizing the frequency for selecting individual tasks. Upon optimization, the statistics of emotion experience becomes stationary.

Towards Flocking Navigation and Obstacle Avoidance for Multi-UAV Systems through Hierarchical Weighting Vicsek Model

Flocking navigation and obstacle avoidance in complex environments remain challenging for multiple unmanned aerial vehicle (multi-UAV) systems, especially when only one UAV (termed as information UAV) knows the predetermined path and the communication range is limited. To this end, we propose a hierarchical weighting Vicsek model (HWVEM). In this model, a hierarchical weighting mechanism and an obstacle avoidance mechanism are designed. Based on the hierarchical weighting mechanism, all the UAVs are divided into different layers, and assigned with different weights according to the layer to which they belong. The purpose is to align the rest of UAVs with the information UAV more efficiently. Subsequently, the obstacle avoidance mechanism that utilizes only the local information is developed to ensure the system safety in an environment filled with obstacles differing in size and shape. A series of simulations have been conducted to demonstrate the high performance of HWVEM in terms of convergence time, success rate, and safety.

Comprehensive Interval-Induced Weights Allocation with Bipolar Preference in Multi-Criteria Evaluation

Preferences-involved evaluation and decision making are the main research subjects in Yager’s decision theory. When the involved bipolar preferences are concerned with interval information, some induced weights allocation and aggregation methods should be reanalyzed and redesigned. This work considers the multi-criteria evaluation situation in which originally only the interval-valued absolute importance of each criterion is available. Firstly, based on interval-valued importance, upper bounds, lower bounds, and the mean points of each, we used the basic unit monotonic function-based bipolar preference weights allocation method four times to generate weight vectors. A comprehensive weighting mechanism is proposed after considering the normalization of the given absolute importance information. The bipolar optimism–pessimism preference-based weights allocation will also be applied according to the magnitudes of entries of any given interval input vector. A similar comprehensive weighting mechanism is still performed. With the obtained weight vector for criteria, we adopt the weighted ordered weighted averaging allocation on a convex poset to organically consider both two types of interval-inducing information and propose a further comprehensive weights allocation mechanism. The detailed comprehensive evaluation procedures with a numerical example for education are presented to show that the proposed models are feasible and useful in interval, multi-criteria, and bipolar preferences-involved decisional environments.

Hierarchical Weighting Vicsek Model for Flocking Navigation of Drones

Flocking navigation, involving alignment-guaranteed path following and collision avoidance against obstacles, remains to be a challenging task for drones. In this paper, we investigate how to implement flocking navigation when only one drone in the swarm masters the predetermined path, instead of all drones mastering their routes. Specifically, this paper proposes a hierarchical weighting Vicsek model (WVEM), which consists of a hierarchical weighting mechanism and a layer regulation mechanism. Based on the hierarchical mechanism, all drones are divided into three layers and the drones at different layers are assigned with different weights to guarantee the convergence speed of alignment. The layer regulation mechanism is developed to realize a more flexible obstacle avoidance. We analyze the influence of the WVEM parameters such as weighting value and interaction radius, and demonstrate the flocking navigation performance through a series of simulation experiments.

Fuzzy rule-based set point weighting for fuzzy PID controller

The objective of this work is to design a fuzzy rule-based set point weighting mechanism for fuzzy PID (FPID) controller so that an overall improved closed-loop performance may be achieved for linear as well as nonlinear process models. Till date, tuning criteria for FPID controllers are not well defined. Trial-and-error approach is primarily adopted and it is quite time-consuming and does not always ensure improved overall closed-loop behaviour. Hence, to ascertain satisfactory closed-loop performance with an initially tuned fuzzy controller, a fuzzy rule-based set point weighting mechanism is reported here. The proposed scheme is capable of providing performance enhancement with instantaneous weighting factor calculated online for each instant based on the latest process operating conditions. The proposed methodology is capable of ascertaining acceptable performances during set point tracking as well as load recovery phases. Efficacy of the proposed scheme is verified for linear as well as nonlinear process models through simulation study along with real-time verification on servo position control in comparison with the others’ reported performance augmentation schemes as well as fuzzy sliding mode control.