Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata Anthony D. Rhodes Portland State University Abstract -- We apply two evolutionary search algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that can perform computational tasks requiring global coordination. In particular, we compare search efficiency for PSO and GAs applied to both the density classification problem and to the novel generation of "chaotic" CA. Our work furthermore introduces a new variant of PSO, the Binary Global-Local PSO (BGL-PSO). I. INTRODUCTION: CELLULAR AUTOMATA Cellular Automata (CA) are discrete, spatially-extended dynamical systems consisting of cells, each of which contains a finite state machine. Given an initial configuration of cells, CA evolve over time by performing computations according to local rules.

Recent years have seen a rising interest in developing AI algorithms for real world big data domains ranging from autonomous cars to personalized assistants. At the core of these algorithms are architectures that combine deep neural networks, for approximating the underlying multidimensional state-spaces, with reinforcement learning, for controlling agents that learn to operate in said state-spaces towards achieving a given objective. The talk will first outline notable past and future efforts in deep reinforcement learning as well as identify fundamental problems that this technology has been struggling to overcome. Towards mitigating these problems (and open up an alternative path to general artificial intelligence), I will then summarize a brain computing model of intelligence, rooted in the latest findings in neuroscience. The talk will conclude with an overview of the recent research efforts in the field of multi-agent systems, to provide the future teams of humans and agents with the necessary tools that allow them to safely co-exist.

The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account of maximizing returns. Therefore, this paper proposes a deep reinforcement learning based trading agent that can manage the portfolio considering not only profit maximization but also risk restraint. We also propose a new target policy to allow the trading agent to learn to prefer low-risk actions. The new target policy can be reflected in the update by adjusting the greediness for the optimal action through the hyper parameter. The proposed trading agent verifies the performance through the data of the cryptocurrency market. The Cryptocurrency market is the best test-ground for testing our trading agents because of the huge amount of data accumulated every minute and the market volatility is extremely large. As a experimental result, during the test period, our agents achieved a return of 1800% and provided the least risky investment strategy among the existing methods. And, another experiment shows that the agent can maintain robust generalized performance even if market volatility is large or training period is short.

This paper proposes a new subspace learning method, named Quantized Fisher Discriminant Analysis (QFDA), which makes use of both machine learning and information theory. There is a lack of literature for combination of machine learning and information theory and this paper tries to tackle this gap. QFDA finds a subspace which discriminates the uniformly quantized images in the Discrete Cosine Transform (DCT) domain at least as well as discrimination of non-quantized images by Fisher Discriminant Analysis (FDA) while the images have been compressed. This helps the user to throw away the original images and keep the compressed images instead without noticeable loss of classification accuracy. We propose a cost function whose minimization can be interpreted as rate-distortion optimization in information theory. We also propose quantized Fisherfaces for facial analysis in QFDA.

This paper presents entropy maps, an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. In particular, entropy maps show the instantaneous level of randomness in decisions of a pedestrian agent situated in a specific point of the simulated environment with an heatmap approach. Experimental results highlighting the relevance of this tool supporting modelers are provided and discussed. Keywords: Data Visualization · Modelling and Simulation · Stochastic Models. 1 Introduction & Related Works Computer simulation of complex systems often employs stochastic models: implied randomness is a way to account for aspects that are potentially relevant to the overall phenomenon but cannot be explicitly considered to keep the model and the modelling phase manageable [3]. Pedestrian and crowd behaviour simulation, for instance, requires considering different kinds of decisions, taken at distinct levels of abstraction, employing heterogeneous information and knowledge about the environment, from path planning [7] to the regulation of distance from other pedestrians and obstacles present in the environment[2,8]. Exploring implications of randomness and situations of indecision, irresolution in case of choice among alternative lines of behaviour such as the exits from an environment in an emergency situation [10], can be a very significant step, with important implications of overall simulation results. This paper presents an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. As in the framework of probability theory [12], we use the concept of entropy to provide a measure of uncertainty over the simulated space The paper, first of all, describes a general decision making model for supporting wayfinding, which comes from previous work by the authors [8,7].

The majority of multi-agent system (MAS) implementations aim to optimise agents' policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems.

Reinforcement Learning (RL) is being increasingly applied to optimize complex functions that may have a stochastic component. RL is extended to multi-agent systems to find policies to optimize systems that require agents to coordinate or to compete under the umbrella of Multi-Agent RL (MARL). A crucial factor in the success of RL is that the optimization problem is represented as the expected sum of rewards, which allows the use of backward induction for the solution. However, many real-world problems require a joint objective that is non-linear and dynamic programming cannot be applied directly. For example, in a resource allocation problem, one of the objective is to maximize long-term fairness among the users. This paper addresses and formalizes the problem of joint objective optimization, where not only the sum of rewards of each agent but a function of the sum of rewards of each agent needs to be optimized. The proposed algorithms at the centralized controller aims to learn the policy to dictate the actions for each agent such that the joint objective function based on average per step rewards of each agent is maximized. We propose both model-based and model-free algorithms, where the model-based algorithm is shown to achieve $\Tilde{O}(\sqrt{\frac{K}{T}})$ regret bound for $K$ agents over a time-horizon $T$, and the model-free algorithm can be implemented using deep neural networks. Further, using fairness in cellular base-station scheduling as an example, the proposed algorithms are shown to significantly outperform the state-of-the-art approaches.

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.

It's late Saturday morning and Mrs. Little enters her usual supermarket, eyes fixed on her watch. In front of her, the aisles are overrun with shopping carts overflowing with all different types of products. She plunges into the crowd, weaving her way between the shoppers and dodging the promotional displays which block the middle of the aisles. Somehow, she manages to pick up two packs of water before fighting her way back to the other end of the store to get some dog food. As her cart becomes heavier, it becomes more difficult to maneuver.

Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving efficient communication among agents has always been an overarching problem in MARL. In this work, we propose Variance Based Control (VBC), a simple yet efficient technique to improve communication efficiency in MARL. By limiting the variance of the exchanged messages between agents during the training phase, the noisy component in the messages can be eliminated effectively, while the useful part can be preserved and utilized by the agents for better performance. Our evaluation using a challenging set of StarCraft II benchmarks indicates that our method achieves $2-10\times$ lower in communication overhead than state-of-the-art MARL algorithms, while allowing agents to better collaborate by developing sophisticated strategies.