A Norm Compliance Approach for Open and Goal‐Directed Intelligent Systems

Abstract

The increasing development of autonomous intelligent systems, such as smart vehicles, smart homes, and social robots, poses new challenges to face. Among them, ensuring that such systems behave lawfully is one of the crucial topics to be addressed for improving their employment in real contexts of daily life. In this work, we present an approach for norm compliance in the context of open and goal-directed intelligent systems working in dynamic normative environments where goals, services, and norms may change. Such an approach complements a goal-directed system modifying its goals and the way to achieve them for taking norms into accounts, thus influencing the practical reasoning process that goal-oriented systems implement for figuring out what to do. The conformity to norms is established at the goal level rather than at the action level. The effect of a norm that acts at the goal level spreads out at the lower level of actions, thus also improving system flexibility. Recovery mechanisms are provided to face exceptional situations that could be caused by normative changes. A case study in the field of the business organizations is presented for demonstrating the strengths of the proposed solution.

Customisable assistive plans as dynamic composition of services with normed-QoS

“…The transition from a Q-AW specification to an N-AW model is obtained by specialising the elements of the abstract layer (see Fig. 2) with a new set of elements that derive from norm-based theoretical foundations proposed in Ribino and Lodato (2019). In particular, in this work, we propose the novel concept of normed-QoS, thus introducing a Norm-oriented Abstract Workflow.…”

Inverse Reinforcement Learning Through Max-Margin Algorithm

“…RL has experienced growth in attention and interest due to promising results in intelligent environments [10][11][12] and the areas like: playing AlphaGo [13], controlling systems in robotics [14][15][16], medical [17], atari [18] and competitive video . A method of investigating challenges posed by reporting procedures, reproducibility and proper experimental techniques through Deep Reinforcement Learning (DRL) is discussed in [19].…”

An Overview of Inverse Reinforcement Learning Techniques

“…ML is an application of AI that focuses on learning and improving itself from experience and without being explicitly programmed. ML emphasizes on developing algorithms that can access data and use it for self-learning [1,2,3] in an intelligent environments [4,5,6]. We are dealing with a certain number of sensors, which enable the IE [7] to be aware of the user's current action and goal.…”