In many practical applications, machine learning is divided over multiple agents, where each agent learns a different task and/or learns from a different dataset. We present Epsilon Model Spaces (EMS), a framework for learning a global model by aggregating local learnings performed by each agent. Our approach forgoes sharing of data between agents, makes no assumptions on the distribution of data across agents, and requires minimal communication between agents. We empirically validate our techniques on MNIST experiments and discuss how EMS can generalize to a wide range of problem settings, including federated averaging and catastrophic forgetting. We believe our framework to be among the first to lay out a general methodology for "combining" distinct models.

This paper describes the deployment and implementation of a blockchain to improve the security, knowledge and intelligence during the inter-agent communication and collaboration processes in restrict domains of the Internet Infrastructure. It is a work that proposes the application of a blockchain, platform independent, on a particular model of agents, but that can be used in similar proposals, once the results on the specific model were satisfactory.

Across consumer-facing industries including hospitality and quality ranking sites, customer service is one of the most critical metrics determining the value of a product against competitors. Planned standards are well and good, but if a process doesn't work or malfunctions, consumers want help, and they want that help to be easy to access, able to handle the problem and resolve it quickly. Moreover, they want anybody (including our often hated self-check-in machines), to be understanding, considerate, and happy to assist. For decades this "human touch" has been the one thing the digital age could not offer, even as the shopping path, and the products themselves have become more and more interwoven with computer and internet technology. The U.S. 3D design company Autodesk has teamed up with Soul Machines, a New Zealand developer of human-like avatars, to produce its first digital customer service agent. AVA (Autodesk virtual agent) is ready to interact with customers 24 hours a day to resolve their concerns.

Control architectures and algorithms for large autonomous swarms are receiving increased research interest. Control of swarm systems becomes more difficult as the size of the agent swarm increases, making centralized control approaches inadequate. This paper presents the informal team assignment algorithm. By leveraging agent roles and signaling actions, the algorithm provides a local agent mechanism leading to the emergence of cooperative teams. Tested in a modified pursuit-evasion domain, simulation results demonstrate that agent roles and inter-agent signaling spontaneously create small collaborative agent teams dedicated to shared task accomplishment. The result is in higher win ratios for signal and role capable swarms.

We examine the effects of inter-agent variation on the ability of a decentralized multi-agent system (MAS) to self-organize in response to dynamically changing task demands. In decentralized biological systems, inter-agent variation as minor as noise has been observed to improve a system's ability to redistribute agent resources in response to external stimuli. We compare the performance of two MAS consisting of agents with and without noisy sensors on a cooperative tracking problem and examine the effects of inter-agent variation on agent behaviors and how those behaviors affect system performance. Results show that small variations in how individual agents respond to stimuli can lead to more accurate and stable allocation of agent resources.

In recent years, we have witnessed a blossoming of research proposals addressing thechallenges in reasoning about action and change in domains that include an agent operatingin a multi-agent setting. In particular, the recent emphasis has been on dealing with domains that involve agents reasoning not only about the state of the world but also about the knowledge andbeliefs of other agents. An open challenge is the management of conflicting and incorrectbeliefs. This paper seeks to introduce a solution to this through the use of doxastic attitudes. Built on top of the action language mA+, we extend the transition functions of an agent to include this idea of attitudes and showcase how these work in two different examples.

We address a problem of area protection in graph-based scenarios with multiple mobile agents where connectivity is maintained among agents to ensure they can communicate. The problem consists of two adversarial teams of agents that move in an undirected graph shared by both teams. Agents are placed in vertices of the graph; at most one agent can occupy a vertex; and they can move into adjacent vertices in a conflict free way. Teams have asymmetric goals: the aim of one team - attackers - is to invade into given area while the aim of the opponent team - defenders - is to protect the area from being entered by attackers by occupying selected vertices. The team of defenders need to maintain connectivity of vertices occupied by its own agents in a visibility graph. The visibility graph models possibility of communication between pairs of vertices. We study strategies for allocating vertices to be occupied by the team of defenders to block attacking agents where connectivity is maintained at the same time. To do this we reserve a subset of defending agents that do not try to block the attackers but instead are placed to support connectivity of the team. The performance of strategies is tested in multiple benchmarks. The success of a strategy is heavily dependent on the type of the instance, and so one of the contributions of this work is that we identify suitable strategies for diverse instance types.

We use an algebraic viewpoint, namely a matrix framework to deal with the problem of resource allocation under uncertainty in the context of a qualitative approach. Our basic qualitative data are a plausibility relation over the resources, a hierarchical relation over the agents and of course the preference that the agents have over the resources. With this data we propose a qualitative binary relation $\unrhd$ between allocations such that $\mathcal{F}\unrhd \mathcal{G}$ has the following intended meaning: the allocation $\mathcal{F}$ produces more or equal social welfare than the allocation $\mathcal{G}$. We prove that there is a family of allocations which are maximal with respect to $\unrhd$. We prove also that there is a notion of simple deal such that optimal allocations can be reached by sequences of simple deals. Finally, we introduce some mechanism for discriminating {optimal} allocations.

We present a simple model of a non-equilibrium self-organizing market where asset prices are partially driven by investment decisions of a bounded-rational agent. The agent acts in a stochastic market environment driven by various exogenous "alpha" signals, agent's own actions (via market impact), and noise. Unlike traditional agent-based models, our agent aggregates all traders in the market, rather than being a representative agent. Therefore, it can be identified with a bounded-rational component of the market itself, providing a particular implementation of an Invisible Hand market mechanism. In such setting, market dynamics are modeled as a fictitious self-play of such bounded-rational market-agent in its adversarial stochastic environment. As rewards obtained by such self-playing market agent are not observed from market data, we formulate and solve a simple model of such market dynamics based on a neuroscience-inspired Bounded Rational Information Theoretic Inverse Reinforcement Learning (BRIT-IRL). This results in effective asset price dynamics with a non-linear mean reversion - which in our model is generated dynamically, rather than being postulated. We argue that our model can be used in a similar way to the Black-Litterman model. In particular, it represents, in a simple modeling framework, market views of common predictive signals, market impacts and implied optimal dynamic portfolio allocations, and can be used to assess values of private signals. Moreover, it allows one to quantify a "market-implied" optimal investment strategy, along with a measure of market rationality. Our approach is numerically light, and can be implemented using standard off-the-shelf software such as TensorFlow.