Autonomous systems can substantially enhance a human's efficiency and effectiveness in complex environments. Machines, however, are often unable to observe the preferences of the humans that they serve. Despite the fact that the human's and machine's objectives are aligned, asymmetric information, along with heterogeneous sensitivities to risk by the human and machine, make their joint optimization process a game with strategic interactions. We propose a framework based on risk-sensitive dynamic games; the human seeks to optimize her risk-sensitive criterion according to her true preferences, while the machine seeks to adaptively learn the human's preferences and at the same time provide a good service to the human. We develop a class of performance measures for the proposed framework based on the concept of regret. We then evaluate their dependence on the risk-sensitivity and the degree of uncertainty. We present applications of our framework to self-driving taxis, and robo-financial advising.

BICA Labs Laboratories for Biologically Inspired Cognitive Architectures 2. Blockchain: Distributed Ledger Technology (DLT) TRUSTED PERSISTENT DATA RECORDS INSIDE TRUSTLESS ENVIRONMENTS WITHOUT CENTRAL GOVERNANCE SECURED BY ECONOMIC INCENTIVES 4. Distributed database (ledger) Distributed computations (state changes), Turing-complete OR incomplete Peer-to-peer mesh network Cryptographically secured TECHNOLOGY ECONOMICS BLOCKCHAIN Multiagent economy Game theory Free open market Non-state decentralized economies linked to particular types of resources or businesses 6. NO CENTRALIZATION 7. NO DATA OLIGOPOLY 9. Blockchain tech intro 10. Important Blockchains Bitcoin Ripple Ethereum ZCashDashNEM Most popular cryptocurrency & source code base Value transfer network Smart contracts Proof of Importance Governance model Zero-knowledge 11. Most important ready-to-go DLTs cores Bitcoin Core Graphene Scorex Tendermint Ripple / Stellar Language C/C C Scala Go C Consensus PoW dPoS PoW, 2x PoS PoS Blockchains Bitcoin, Dash, Litecoin, ... BitShares, Steem, Golos – (Waves experiments) Cosmos (under dev) Ripple, Stellar, Infra (e-Auction) " " Most proved Blazingly fast Modular PoS Fast & proved "–" Hard to understand Complex model Limited functionality, no real-world impl Immature Complex model 16. Languages for working with DLTs cores C/C -- Bitcoin, Graphene, Ripple: performance Go -- Ethereum, Tehndermint Python -- Ethereum experiments with blockchain Rust -- Ethereum, Bitcoin: performance efficient code Scala -- Scorex (fast blockchain prototyping) Java -- NEM JavaScript (Angular, React): UI & APIs 17. Meta-languages for smart contracts Solidity: JavaScript-like Serpent: Python-like Viper: Python-like (Serpent 2.0) 18. Blockchain & AI Technical Synergy 19. Civilization 4.0 key factors Quantum Computing Generic Artificial Intelligence Transhumanism Life extension Cyborgization Cosmic Expansion SINGULARITY 31.

The buzzwords "Industry 4.0" and "digital business" represent the start of a complex transformational process that will deeply affect industry and society during the next decade. This transformation is based on the convergence of the real (analog) world and the virtual (digital) world by means of machineto- machine (M2M) communication, autonomous systems (for example, robotics) and the Internet of Things (IoT). The German government uses the term "Industry 4.0" as the title of a government project promoting the computerization of traditional industries and the creation of intelligent factories (smart factories) that will be supported by cyberphysical systems and the IoT. The digits "4.0" in Industry 4.0 stand for the fourth industrial revolution: the transition of production from digital processing to fully interconnected processes, products and services. It follows the evolution of production processes for tradable goods from manufacturing to industry production (the first revolution), the move from steam-driven machine production to electricity-driven production (the second revolution) and the shift from analog processing to digital processing and microelectronics (the third revolution). One of the major features of Industry 4.0 is the ability of machines and devices to communicate with each other without a human interface.

Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.

A computer algorithm equipped with a form of artificial curiosity can learn to solve tricky problems even when it isn't immediately clear what actions might help it reach this goal. Researchers at the University of California, Berkeley, developed an "intrinsic curiosity model" to make their learning algorithm work even when there isn't a strong feedback signal. The curiosity model developed by this team sees the AI software controlling a virtual agent in a video game seek to maximize its understanding of its environment and especially aspects of that environment that affect it. There have been previous efforts to give AI agents curiosity, but these have tended to work in a more simplistic way. The trick may help address a shortcoming of today's most powerful machine-learning techniques, and it could point to ways of making machines better at solving real-world problems.

In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learning process, agents may link these interruptions, that impact their reward, to specific states and deliberately avoid them. The situation is particularly challenging in a multi-agent context because agents might not only learn from their own past interruptions, but also from those of other agents. Orseau and Armstrong defined \emph{safe interruptibility} for one learner, but their work does not naturally extend to multi-agent systems. This paper introduces \textit{dynamic safe interruptibility}, an alternative definition more suited to decentralized learning problems, and studies this notion in two learning frameworks: \textit{joint action learners} and \textit{independent learners}. We give realistic sufficient conditions on the learning algorithm to enable dynamic safe interruptibility in the case of joint action learners, yet show that these conditions are not sufficient for independent learners. We show however that if agents can detect interruptions, it is possible to prune the observations to ensure dynamic safe interruptibility even for independent learners.

We consider abstract-argumentation-theoretic coalition formability in this work. Taking a model from political alliance among political parties, we will contemplate profitability, and then formability, of a coalition. As is commonly understood, a group forms a coalition with another group for a greater good, the goodness measured against some criteria. As is also commonly understood, however, a coalition may deliver benefits to a group X at the sacrifice of something that X was able to do before coalition formation, which X may be no longer able to do under the coalition. Use of the typical conflict-free sets of arguments is not very fitting for accommodating this aspect of coalition, which prompts us to turn to a weaker notion, conflict-eliminability, as a property that a set of arguments should primarily satisfy. We require numerical quantification of attack strengths as well as of argument strengths for its characterisation. We will first analyse semantics of profitability of a given conflict-eliminable set forming a coalition with another conflict-eliminable set, and will then provide four coalition formability semantics, each of which formalises certain utility postulate(s) taking the coalition profitability into account.

This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters. For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.

In this work, we present a methodology that enables an agent to make efficient use of its exploratory actions by autonomously identifying possible objectives in its environment and learning them in parallel. The identification of objectives is achieved using an online and unsupervised adaptive clustering algorithm. The identified objectives are learned (at least partially) in parallel using Q-learning. Using a simulated agent and environment, it is shown that the converged or partially converged value function weights resulting from off-policy learning can be used to accumulate knowledge about multiple objectives without any additional exploration. We claim that the proposed approach could be useful in scenarios where the objectives are initially unknown or in real world scenarios where exploration is typically a time and energy intensive process. The implications and possible extensions of this work are also briefly discussed.

This paper explores how an autonomous agent can model dynamic environments and use that knowledge to improve its behavior. This capability is of particular importance for persistent agents, or long-term autonomy. Inspiration is drawn from circadian rhythms in nature, which drive periodic behavior in many organisms. In our approach, the chemical oscillators from nature are replaced with methods from time series analysis designed for forecasting complex season patterns. This model is incorporated into a behavior-based architecture as an advanced-percept, providing future estimates of the environment rather than current measurements. A simulated application of a janitor robot working in an environment with heavy pedestrian traffic was created as a testbed. Experimental data used real world pedestrian traffic counts and showed an agent using online forecasting of future traffic outperformed both a reactive, sensor-based, strategy and a strategy with a deterministic schedule.