LENNA (Learning Emotions Neural Network Assisted): an empathic chatbot designed to study the simulation of emotions in a bot and their analysis in a conversation

Currently, most chatbots are unable to detect the emotional state of the interlocutor and respond according to the interlocutor’s emotional state. Over the last few years, there has been growing interest in empathic chatbots. In other disciplines aside from artificial intelligence, e.g., in medicine, there is growing interest in the study and simulation of human emotions. However, there is a fundamental issue that is not commonly addressed, and it is the design of protocols for quantitatively evaluating an empathic chatbot by utilizing the analysis of the conversation between the bot and an interlocutor. This study is motivated by the aforementioned scenarios and by the lack of methods for assessing the performance of an empathic bot; thus, a chatbot with the ability to recognize the emotions of its interlocutor is needed. The main novelty of this study is the protocol with which it is possible to analyze the conversations between a chatbot and an interlocutor, regardless of whether the latter is a person or another chatbot. For this purpose, we have designed a minimally viable prototype of an empathic chatbot, named LENNA, for evaluating the usefulness of the proposed protocol. The proposed approach uses Shannon entropy to measure the changes in the emotional state experienced by the chatbot during a conversation, applying sentiment analysis techniques to the analysis of the conversation. Once the simulation experiments were performed, the conversations were analyzed by applying multivariate statistical methods and Fourier analysis. We show the usefulness of the proposed methodology for evaluating the emotional state of LENNA during conversations, which could be useful in the evaluation of other empathic chatbots

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

SAFEL - A Situation-aware Fear Learning Model

This thesis proposes a novel and robust online adaptation mechanism for threat prediction and prevention capable of taking into consideration complex contextual and temporal information in its internal learning processes. The proposed mechanism is a hybrid cognitive computational model named SAFEL (Situation-Aware FEar Learning), which integrates machine learning algorithms with concepts of situation-awareness from expert systems to simulate both the cued and contextual fear-conditioning phenomena. SAFEL is inspired by well-known neuroscience findings on the brain's mechanisms of fear learning and memory to provide autonomous robots with the ability to predict undesirable or threatening situations to themselves. SAFEL's ultimate goal is to allow autonomous robots to perceive intricate elements and relationships in their environment, learn with experience through autonomous environmental exploration, and adapt at execution time to environmental changes and threats. SAFEL consists of a hybrid architecture composed of three modules, each based on a different approach and inspired by a different region (or function) of the brain involved in fear learning. These modules are: the Amygdala Module (AM), the Hippocampus Module (HM) and the Working Memory Module (WMM). The AM learns and detects environmental threats while the HM makes sense of the robot's context. The WMM is responsible for combining and associating the two types of information processed by the AM and HM. More specifically, the AM simulates the cued conditioning phenomenon by creating associations between co-occurring aversive and neutral environmental stimuli. The AM represents the kernel of emotional appraisal and threat detection in SAFEL's architecture. The HM, in turn, handles environmental information at a higher level of abstraction and complexity than the AM, which depicts the robot's situation as a whole. The information managed by the HM embeds in a unified representation the temporal interactions of multiple stimuli in the environment. Finally, the WMM simulates the contextual conditioning phenomenon by creating associations between the contextual memory formed in the HM and the emotional memory formed in the AM, thus giving emotional meaning to the contextual information acquired in past experiences. Ultimately, any previously experienced pattern of contextual information triggers the retrieval of that stored contextual memory and its emotional meaning from the WMM, warning the robot that an undesirable situation is likely to happen in the near future. The main contribution of this work as compared to the state of the art is a domain-independent mechanism for online learning and adaptation that combines a fear-learning model with the concept of temporal context and is focused on real-world applications for autonomous robotics. SAFEL successfully integrates a symbolic rule-based paradigm for situation management with machine learning algorithms for memorizing and predicting environmental threats to the robot based on complex temporal context. SAFEL has been evaluated in several experiments, which analysed the performance of each module separately. Ultimately, we conducted a comprehensive case study in the robot soccer scenario to evaluate the collective work of all modules as a whole. This case study also analyses to which extent the emotional feedback of SAFEL can improve the intelligent behaviour of a robot in a practical real-world situation, where adaptive skills and fast/flexible decision-making are crucial

Bryn Mawr College Undergraduate Catalog 2008-09

https://repository.brynmawr.edu/bmc_calendars/1066/thumbnail.jp

Bryn Mawr College Undergraduate Catalog 2008-09

https://repository.brynmawr.edu/bmc_calendars/1066/thumbnail.jp