NEW YORK – For a dozen students from Futaba Future High School in Fukushima Prefecture, a recent visit to the United Nations was a chance to share their plans to improve the lives of others by drawing from their catastrophic earthquake and tsunami experiences as a source of strength. Despite overcoming enormous hurdles in the aftermath of the March 11, 2011, disaster that took more than 19,000 lives, the surviving students have moved forward with aspirations of choosing future paths to benefit the global community. "Thanks to all my experiences like getting bullied, joining the drama club and studying at my high school, I think I could grow well," Satsuki Sekine told U.N. diplomats, staff and youth representatives who gathered to hear their presentation on the current situation in Fukushima early this month as part of a scheduled visit while in New York. The 17-year-old explained how drama can be used to portray the challenges of discrimination and conflict "not as an abstract concept but with specific and visual examples." Recounting how the tsunami rendered her home unlivable, she explained how her life in Tomioka as a normal 9-year-old was turned upside down.

Problem domains such as disaster relief, search and rescue, and games can benefit from having a human quickly train coordinated behaviors for a diverse set of agents. Hierarchical Training of Agent Behaviors (HiTAB) is a Learning from Demonstration (LfD) approach that addresses some inherent complexities in multiagent learning, making it possible to train complex heterogeneous behaviors from a small set of training samples. In this paper, we successfully demonstrate LfD training of formation behaviors using a small set of agents that, without retraining, continue to operate correctly when additional agents are available. We selected training of formations for the experiments because formations: require a great deal of coordination between agents, are heterogenous due to the differing roles of participating agents, and can scale as the number of agents grows. We also introduce some extensions to HiTAB that facilitate this type of training.

In our talk we discuss Meta-agents frameworks for working within the Internet of Things (IoT) systems. In particular we discuss our own Meta-agent system called SENtry Agents (SAGE). We discuss why such systems must follow Simon’s laws of the Artificial, and because of that must be Holonic.

We address the question of how to build AI agents that behave ethically by appealing to a computational creativity framework in which output artifacts are agent behaviors and candidate behaviors are evaluated using a normative ethics as the aesthetic measure. We then appeal again to computational creativity to address the meta-level question of which normative ethics the system should employ as its aesthetic, where now output meta-artifacts are normative ethics and candidate ethics are evaluated using a meta-ethics-based aesthetic. We consider briefly some of the issues raised by such a proposal as well as how the hybrid base-meta-level system might be evaluated from three different perspectives: creative, behavioral and ethical.

Many scenarios involve a tension between individual interest and the interests of others. Such situations are called social dilemmas. Because of their ubiquity in economic and social interactions constructing agents that can solve social dilemmas is of prime importance to researchers interested in multi-agent systems. We discuss why social dilemmas are particularly difficult, propose a way to measure the 'success' of a strategy, and review recent work on using deep reinforcement learning to construct agents that can do well in both perfect and imperfect information bilateral social dilemmas.

We present a brief overview of learning dynamics for anti-coordination in ad-hoc scenarios. Specifically, we consider multi-armed bandit algorithms, reinforcement learning, and symmetric strategies for the repeated resource allocation game. In a multi-agent system with dynamic population where every agent is able to learn, the anti-coordination problem exhibits unique challenges. Thus, it is essential for the success of a joint plan that the agents can quickly and robustly learn their optimal behavior. In this work we will focus on convergence rate, efficiency, and fairness in the final outcome.

This paper describes the architecture that integrates DALI MASs (Multi-Agent Systems) and ASP (Answer Set Programming) modules for reaching goals in a flexible and timely way, where DALI is a computational-logic-based fully implemented agent-oriented logic programming language and ASP modules includes solvers that allow affordable and flexible planning capabilities. The proposed DALI MAS architecture exploits such modules for flexible goal decomposition and planning, with the possibility to select plans according to a suite of possible preferences and to re-plan upon need. We present an abstract case-study concerning DALI agents which cooperate for exploring an unknown territory under changing circumstances in an optimal or at least sub-optimal fashion. The architecture can be exploited not only by DALI agents, but rather by any kind of logical agent.

Internet of things (IoT), from heart monitoring implants to home heating control systems, are becoming an integral part of our daily lives. We expect these technologies to become smarter, able to autonomously reason, act, and communicate with other entities in the environment and act to achieve shared goals. To realize the full potential of these systems, we need to understand the mechanisms that allow multiple agents to effectively operate in changing and uncertain environments. This paper presents a framework that postulates that optimal multi-agent systems achieve adaptive behaviors by minimizing the team’s free energy, where energy minimization process consists of incremental perception (inference) and control (action) phases. We discuss instantiation of this mechanism for a problem of joint distributed decision making, provide the concomitant abstractions and computational mechanisms, and present experimental evidence that energy-based agent teams significantly outperform utility-based teams. We discuss different adaptation mechanisms and scales, explain agent interdependencies produced by energy-based modeling, and look at the role of learning in the adaptation process. We hypothesize that to efficiently operate in uncertain and changing environments, IoT devices must not only maintain enough intelligence to perceive and act locally, but also possess team-level adaptation primitives. We posit that such primitives must embody energy-minimizing mechanisms but can be locally defined without the need for agents to possess global team-level objectives or constraints.

For the Internet of Everything (IoE), from an AI perspective, we discuss the meaning, value and effect that the internet of things (IoT) is expected to have on ordinary life, in industry (IIoT), on the battlefield (IoBT), in the medical field (IoMT) and with intelligent-agent feedback in the form of constructive and destructive interference (IoIT). We consider the topic open-ended but with an AI perspective that addresses how the IoE affects sensing, perception, cognition and behavior, or causal relations whether the context is clear or uncertain for mundane decisions, complex decisions on the battlefield, life and death decisions in the medical arena, or decisions affected by intelligent agents and machines. We pay attention to theoretical perspectives for how these “things” may affect individuals, teams and society; and in turn how they may affect these “things”. We are most interested in what may happen when these “things” begin to think. Our ultimate goal is to use AI to advance autonomy and autonomous characteristics to improve the performance of individual agents and hybrid teams of humans, machines, and robots for the betterment of society.

We examine moral decision making in autonomous systems as inspired by a central question posed by Rossi with respect to moral preferences: can AI systems based on statistical machine learning (which do not provide a natural way to explain or justify their decisions) be used for embedding morality into a machine in a way that allows us to prove that nothing morally wrong will happen? We argue for an evaluation which is held to the same standards as a human agent, removing the demand that ethical behavior is always achieved. We introduce four key meta-qualities desired for our moral standards, and then proceed to clarify how we can prove that an agent will correctly learn to perform moral actions given a set of samples within certain error bounds. Our group-dynamic approach enables us to demonstrate that the learned models converge to a common function to achieve stability. We further explain a valuable intrinsic consistency check made possible through the derivation of logical statements from the machine learning model. In all, this work proposes an approach for building ethical AI systems, coming from the perspective of artificial intelligence research, and sheds important light on understanding how much learning is required in order for an intelligent agent to behave morally with negligible error.