Multi-Agent Modeling for Integrated Process Planning and Scheduling

Multi-agent systems have been used for modelling various problems in the social, biological and technical domain. When comes to technical systems, especially manufacturing systems, agents are most often applied in optimization and scheduling problems. Traditionally, scheduling is done after creation of process plans. In this paper, agent methodology is used for integration of these two functions. The proposed multi-agent architecture provides simultaneous performance of process planning and scheduling and it consists of four intelligent agents: part and job agents, machine agent, and optimization agent. Verification and feasibility of a proposed approach is conducted using agent based simulation in AnyLogic software

Multi-Agent Modeling for Integrated Process Planning and Scheduling

Multi-agent systems have been used for modelling various problems in the social, biological and technical domain. When comes to technical systems, especially manufacturing systems, agents are most often applied in optimization and scheduling problems. Traditionally, scheduling is done after creation of process plans. In this paper, agent methodology is used for integration of these two functions. The proposed multi-agent architecture provides simultaneous performance of process planning and scheduling and it consists of four intelligent agents: part and job agents, machine agent, and optimization agent. Verification and feasibility of a proposed approach is conducted using agent based simulation in AnyLogic software

Methodological comparison of agent models

Hybrid agent architectures comprise the radical change of paradigms in AI over the past decades by reconciling the different styles of reactive, deliberative, even social systems. They have been successfully applied to a range of complex real-world domains. Due to their originally informal background, a verification of design goals in derived implementations, theoretical foundations, and a detailed comparison with other agent models have not yet been obvious. The present work proposes a formal methodology to bridge the gap between theoretical and practical aspects especially of hybrid designs, such as the layered INTERRAP. The employed, connected stages of specification, i.e., architecture, computational model, theory, proof calculus, and implementation, also provide a yet unique framework for comparing heterogeneous agent models including unified and logic-based ones. Based on recent work on INTERRAP, we demonstrate that this methodology allows to compare state-of-the-art designs from robotics, AI, computer science, and cognitive science with respect to a spectrum of inherent properties along the two dimensions of abstraction and declarativity. This supports our claim that INTERRAP is a coherent and advanced account of layered agency including goal-oriented abstraction planning in on-line interaction with reactive skills and social reasoning. We also derive particular research issues to guide the future development of INTERRAP

Game Theory Models for the Verification of the Collective Behaviour of Autonomous Cars

The collective of autonomous cars is expected to generate almost optimal traffic. In this position paper we discuss the multi-agent models and the verification results of the collective behaviour of autonomous cars. We argue that non-cooperative autonomous adaptation cannot guarantee optimal behaviour. The conjecture is that intention aware adaptation with a constraint on simultaneous decision making has the potential to avoid unwanted behaviour. The online routing game model is expected to be the basis to formally prove this conjecture.Comment: In Proceedings FVAV 2017, arXiv:1709.0212