Social Satisficing: Multi-agent reinforcement learning with satisficing agents.

For a reinforcement learning agent to finish trial-and-error in a realistic time duration, it is necessary to limit the scope of exploration during the learning process. However, limiting the exploration scope means limitation in optimality: the agent could fall into a suboptimal solution. This is the nature of local, bottom-up way of learning. An alternative way is to set a goal to be achieved, which is a more global, top-down way. The risk-sensitive satisficing (RS) value function incorporate, as a method of the latter way, the satisficing principle into reinforcement learning and enables agents to quickly converge to exploiting the optimal solution without falling into a suboptimal one, when an appropriate goal (aspiration level) is given. However, how best to determine the aspiration level is still an open problem. This study proposes social satisficing, a framework for multi-agent reinforcement learning which determines the aspiration level through information sharing among multiple agents. In order to verify the effectiveness of this novel method, we conducted simulations in a learning environment with many suboptimal goals (SuboptimaWorld). The results show that the proposed method, which converts the aspiration level at the episodic level into local (state-wise) aspiration levels, possesses a higher learning efficiency than any of the compared methods, and that the novel method has the ability to autonomously adjust exploration scope, while keeping the shared information minimal. This study provides a glimpse into an aspect of human and biological sociality which has been mentioned little in the context of artificial intelligence and machine learning.

Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach.

In general, it is common knowledge that people's feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician's diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

Performance Evaluation of a Voice-Based Depression Assessment System Considering the Number and Type of Input Utterances.

It is empirically known that mood changes affect facial expressions and voices. In this study, the authors have focused on the voice to develop a method for estimating depression in individuals from their voices. A short input voice is ideal for applying the proposed method to a wide range of applications. Therefore, we evaluated this method using multiple input utterances while assuming a unit utterance input. The experimental results revealed that depressive states could be estimated with sufficient accuracy using the smallest number of utterances when positive utterances were included in three to four input utterances.