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This paper presents a novel semantics for the mA* epistemic action language that takes into consideration dynamic per-agent observability of events. Different from the original mA* semantics, the observability of events is defined locally at the level of possible worlds, giving a new method for compiling event models. Locally defined observability represents agents' uncertainty and false-beliefs about each others' ability to observe events. This allows for modeling second-order false-belief tasks where one agent does not know the truth about another agent's observations and resultant beliefs. The paper presents detailed constructions of event models for ontic, sensing, and truthful announcement action occurrences and proves various properties relating to agents' beliefs after the execution of an action. It also shows that the proposed approach can model second order false-belief tasks and satisfies the robustness and faithfulness criteria discussed by Bolander (2018, https://doi.org/10.1007/978-3-319-62864-6_8). 

This paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems. 

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations. 

Earlier epistemic planning systems for multi-agent domains generate plans that contain various types of actions such as ontic, sensing, or announcement actions. However, none of these systems consider untruthful announcements, i.e., none can generate plans that contain a lying or a misleading announcement. In this paper, we present a novel epistemic planner, called EFP3.0, for multi-agent domains with untruthful announcements. The planner is similar to the systems EFP or EFP2.0 in that it is a forward-search planner and can deal with unlimited nested beliefs and common knowledge by employing a Kripke based state representation and implementing an update model based transition function. Different from EFP, EFP3.0 employs a specification language that uses edge-conditioned update models for reasoning about effects of actions in multi-agent domains. We describe the basics of EFP3.0 and conduct experimental evaluations of the system against state-of-the-art epistemic planners. We discuss potential improvements that could be useful for scalability and efficiency of the system.