Artificial Agents and Speculative Bubbles

Pertaining to Agent-based Computational Economics (ACE), this work presents two models for the rise and downfall of speculative bubbles through an exchange price fixing based on double auction mechanisms. The first model is based on a finite time horizon context, where the expected dividends decrease along time. The second model follows the {\em greater fool} hypothesis; the agent behaviour depends on the comparison of the estimated risk with the greater fool's. Simulations shed some light on the influent parameters and the necessary conditions for the apparition of speculative bubbles in an asset market within the considered framework

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

Towards Designing Artificial Universes for Artificial Agents under Interaction Closure

We are interested in designing artificial universes for artificial agents. We view artificial agents as networks of highlevel processes on top of of a low-level detailed-description system. We require that the high-level processes have some intrinsic explanatory power and we introduce an extension of informational closure namely interaction closure to capture this. Then we derive a method to design artificial universes in the form of finite Markov chains which exhibit high-level processes that satisfy the property of interaction closure. We also investigate control or information transfer which we see as an building block for networks representing artificial agent

HoME: a Household Multimodal Environment

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.Comment: Presented at NIPS 2017's Visually-Grounded Interaction and Language Worksho

A Value-Sensitive Design Approach to Intelligent Agents

This chapter proposed a novel design methodology called Value-Sensitive Design and its potential application to the field of artificial intelligence research and design. It discusses the imperatives in adopting a design philosophy that embeds values into the design of artificial agents at the early stages of AI development. Because of the high risk stakes in the unmitigated design of artificial agents, this chapter proposes that even though VSD may turn out to be a less-than-optimal design methodology, it currently provides a framework that has the potential to embed stakeholder values and incorporate current design methods. The reader should begin to take away the importance of a proactive design approach to intelligent agents

Machine art or machine artists? Dennett, Danto, and the expressive stance

As art produced by autonomous machines becomes increasingly common, and as such machines grow increasingly sophisticated, we risk a confusion between art produced by a person but mediated by a machine, and art produced by what might be legitimately considered a machine artist. This distinction will be examined here. In particular, my argument seeks to close a gap between, on one hand, a philosophically grounded theory of art and, on the other hand, theories concerned with behavior, intentionality, expression, and creativity in natural and artificial agents. This latter set of theories in some cases addresses creative behavior in relation to visual art, music, and literature, in the frequently overlapping contexts of philosophy of mind, artificial intelligence, and cognitive science. However, research in these areas does not typically address problems in the philosophy of art as a central line of inquiry. Similarly, the philosophy of art does not typically address issues pertaining to artificial agents