Maurice Grinberg

Simulating Context Effects in Problem Solving with AMBR

This paper presents a computer simulation of context effects on problem solving with AMBR -- a model of human analogy-making. It demonstrates how perceiving some incidental objects from the environment may change the way the problem is being solved. It also shows that the timing of this perception is important: while the context element may have crucial influence during the initial stages of problem solving it has virtually no effect during the later stages. The simulation also explores the difference between an explicit hint condition where the focus of attention is drawn towards a context situation which is analogous to the target problem and an implicit context condition where an arbitrary object from the environment makes us remind an old episode.

The Interplay of Analogy-Making with Active Vision and Motor Control in Anticipatory Robots

This chapter outlines an approach to building robots with anticipatory behavior based on analogies with past episodes. Anticipatory mechanisms are used to make predictions about the environment and to control selective attention and top-down perception. An integrated architecture is presented that perceives the environment, reasons about it, makes predictions and acts physically in this environment. The architecture is implemented in an AIBO robot. It successfully finds an object in a house-like environment. The AMBR model of analogy-making is used as a basis, but it is extended with new mechanisms for anticipation related to analogical transfer, for top down perception and selective attention. The bottom up visual processing is performed by the IKAROS system for brain modeling. The chapter describes the first experiments performed with the AIBO robot and demonstrates the usefulness of the analogy-based anticipation approach.

Building robots with analogy-based anticipation

A new approach to building robots with anticipatory behavior is presented. This approach is based on analogy with a single episode from the past experience of the robot. The AMBR model of analogy-making is used as a basis, but it is extended with new agent-types and new mechanisms that allow anticipation related to analogical transfer. The role of selective attention on retrieval of memory episodes is tested in a series of simulations and demonstrates the context sensitivity of the AMBR model. The results of the simulations clearly demonstrated that endowing robots with analogy-based anticipatory behavior is promising and deserves further investigation.

Biosignal Based Emotion Analysis of Human-Agent Interactions

A two-phase procedure, based on biosignal recordings, is applied in an attempt to classify the emotion valence content in human-agent interactions. In the first phase, participants are exposed to a sample of pictures with known valence values (taken from IAPS dataset) and classifiers are trained on selected features of the biosignals recorded. During the second phase, biosignals are recorded for each participant while watching video clips with interactions with a female and male ECAs. The classifiers trained in the first phase are applied and a comparison between the two interfaces is carried on based on the classifications of the emotional response from the video clips. The results obtained are promising and are discussed in the paper together with the problems encountered, and the suggestions for possible future improvement.

Emotions and Moral Judgment: A Multimodal Analysis

Recent findings in the field of moral psychology suggest that moral judgment results both from emotional processing and deliberate reasoning. The experimental study uses artificial situations that pose moral dilemmas – a human life have to be sacrificed in order to save more lives. Two factors (physical directness of harm and inevitability of death) are varied in order to explore potential differences in emotional processing and their effects on judgment. Multimodal data is collected and analyzed: moral judgments, skin conductance (as a somatic index of affective processing), and response times (as providing information on deliberation process). Personal-impersonal distinction and inevitability of death are found to influence emotions and judgments in moral dilemmas.

The triple model: Combining cutting-edge web technologies with a cognitive model in an ECA

This paper introduces a new model which is intended to combine the power of a connectionist engine based on fast matrix calculation, RDF based memory and inference mechanisms, and affective computing in a hybrid cognitive model. The model is called triple and has the following three parts: a reasoning module making advantage of RDF based long-term memory by performing fast inferences, a fast connectionist mapping engine, which can establish relevance and similarities, including analogies, and an emotional module which modulates the functioning of the model. The reasoning engine synchronizes the connectionist and the emotional modules which run in parallel, and controls the communication with the user, retrieval from memory, transfer of knowledge, and action execution. The most important cognitive aspects of the model are context sensitivity, specific experiential episodic knowledge and learning. At the same time, the model provides mechanisms of selective attention and action based on anticipation by analogy. The inference and the connectionist modules can be optimized for high performance and thus ensure the real-time usage of the model in agent platforms supporting embodied conversational agents.

Integrating emotions in the TRIPLE ECA model

This paper presents the introduction of emotion-based mechanisms in the TRIPLE ECA model. TRIPLE is a hybrid cognitive model consisting of three interacting modules – the reasoning, the connectionist, and the emotion engines – running in parallel. The interplay between these three modules is discussed in the paper with a focus on the role and implementation of the emotion engine which is based on the FAtiMA agent architecture. The influence of emotions in TRIPLE is related to the volume of the working memory, the speed of the inference mechanisms, the interaction between the reasoning and the connectionist engine, and the connectionist engine itself. Emotions will increase the most important cognitive aspects of the model like context sensitivity, rich experiential episodic knowledge and anticipatory mechanisms.

Investigation of context effects in iterated prisoner's dilemma game

Context effects during Prisoners Dilemma (PD) game playing are investigated. The Cooperation Index (CI) – a quantity computed as a relation between payoffs – defines a cooperativeness scale, along which PD games can be distributed. Context is selected by manipulating the CI range of the games played. It is found that the level of cooperation depends not only on the CI of the current game but also on the CI of the other games in the sequence. The influence of context on the full CI scale is investigated by introducing probe games covering the whole CI range. The results are compared with the prediction of a model that takes into account the current game, the previous play history, and the predicted opponents move. The model is found to be sensitive to both the CI and to the CI of the other games form the game set and a very good agreement between the model and the experimental data was found.

Endowing Artificial Systems with Anticipatory Capabilities: Success Cases

This book has provided various theoretical perspectives on anticipatory processes in natural and artificial cognitive systems. Advantages have been proposed and confirmed in various detailed case studies, which may have given the reader detailed insights into anticipatory processes and their importance in various cognitive systems tasks. To wrap up these advantages and give a concluding overview of various current anticipatory process advantages, this final chapter highlights a concise collection of precise success stories of anticipations in artificial cognitive systems. We survey fourteen case studies, which were developed during the EU project MindRACES. In these studies, simulated or real robots were tested in different environmental tasks, which required advanced sensorimotor and cognitive abilities. These abilities included the initiation and control of goal-directed actions, the orientation of attention, finding and reaching goal locations, and performing mental experiments for action selection. All the studies have shown advantages of anticipatory mechanisms compared to reactive mechanisms in terms of increased robot autonomy and adaptivity. In some cases, anticipations even caused the development of new cognitive abilities, which were simply impossible without anticipatory mechanisms. For each case study, we indicate relevant associated publications, in which the interested reader may find further details on the relevant computational architectures, the involved anticipatory mechanisms, as well as on the analytical and quantitative results. While the book as a whole has laid out the theoretical principles and design methodology for such advancements, this final chapter thus provides various possible starting points for further developments in both the surveyed system architectures and the presented solutions to the cognitive tasks addressed.

Influence of Induced Mood on the Rating of Emotional Valence and Intensity of Facial Expressions

The current study investigates the influence of mood (sad, happy, or neutral) on the valence/intensity ratings of facial expressions of sad, happy, and neutral emotions. The study uses video clips for mood induction and color photographs of emotional facial expressions. Under these conditions, the results show that participants give more extreme ratings to the emotion displayed (happy or sad) when in happy or sad mood without mood-congruence effects. This effect supports the conclusion that arousal alone may play a role in emotion valence/intensity rating (in contrast to results showing mood congruence in other tasks like emotion recognition and detection of emotional expression change). The explanation proposed in the paper is that experienced arousal might guide judgments about the intensity of emotions expressed by other people – when in a more aroused state, a person tends judge that other people also experience more intense emotions.