Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/

Cog and the Creativity of God

The construction of a humanoid robot may be within reach. The science of artificial intelligence (AI) offers new understandings to contemporary Christian theology. First of all, the emerging field of embodied intelligence discloses the wholeness of the human being, correcting the tendency in Christian theology toward an anthropological dualism of body and soul. Secondly, artificial intelligence offers fresh understandings of the human mind, with implications for how human creativity reflects the creativity of God

Conference Models to Bridge Micro and Macro Studies of Science

We propose using community-centered analyses and agent-based models of scientific gatherings such as conferences, symposia and workshops as a way to understand how scientific practices evolve and transition between local, community, and systems levels in science. We suggest using robotics as a case study of global, cross-cultural, interdisciplinary scientific practice. What is needed is a set of modeling frameworks for simulating both the internal and population dynamics of scientific gatherings. In this paper we make the case for conference models as a mid-level unit of analysis that can advance the ways scientists and citizens design systems for transferring and producing knowledge.Science of Science, Conferences, Community-Based Complex Models, Group Size, Methodology

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

Semantic labeling of places using information extracted from laser and vision sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

Realization of reactive control for multi purpose mobile agents

Mobile robots are built for different purposes, have different physical size, shape, mechanics and electronics. They are required to work in real-time, realize more than one goal simultaneously, hence to communicate and cooperate with other agents. The approach proposed in this paper for mobile robot control is reactive and has layered structure that supports multi sensor perception. Potential field method is implemented for both obstacle avoidance and goal tracking. However imaginary forces of the obstacles and of the goal point are separately treated, and then resulting behaviors are fused with the help of the geometry. Proposed control is tested on simulations where different scenarios are studied. Results have confirmed the high performance of the method