Society-in-the-Loop: Programming the Algorithmic Social Contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'Comment: (in press), Ethics of Information Technology, 201

Human Management of the Hierarchical System for the Control of Multiple Mobile Robots

In order to take advantage of autonomous robotic systems, and yet ensure successful completion of all feasible tasks, we propose a mediation hierarchy in which an operator can interact at all system levels. Robotic systems are not robust in handling un-modeled events. Reactive behaviors may be able to guide the robot back into a modeled state and to continue. Reasoning systems may simply fail. Once a system has failed it is difficult to re-start the task from the failed state. Rather, the rule base is revised, programs altered, and the task re-tried from the beginning

Asimovian Adaptive Agents

The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results

Autonomous Agents as Tools for Modeling and Building Complex Control Systems that Operate in Dynamic and Unpredictable Environment

Complex control systems that operate in not entirely predictable environment have to deal with this environment in an autonomous manner using adaptability, the ability to predict environmental changes, and to maintain their integrity. Elements of the system must be able to find a new solution in a dynamic way. In this paper, we present the modeling of a traffic lights’ control system using a multivalent system. This is a large-scale distributed system, consisting of autonomous and rational traffic light agents, in which there is no centre imposing an outcome. Multiagent system brings another kind of organization of the distributed control. The information is distributed over the agents. The behavior of the other agents is incorporated into the making decision process of the agent. We apply different control algorithms in our multiagent simulation environment and show that using multiagent systems in dynamic and unpredictable environment the control will be adoptable

Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges

This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented