As humans and agents or machines are utilized to accomplish missions in multi domain battles there is an increase in artificial intelligence (AI) and machine learning (ML) to support the interaction. However, the techniques and algorithms within AI/ML are not without challenges. One key challenge is how uncertainty of the results influences decision making. Added to this challenge is where uncertainty is introduced and how it impacts the decision making tasks. Uncertainty can come from limitations in the data that is used to develop or train the AI/ML model to lack of confidence in the behavior or suggestions that the agents generate. Uncertainty can come from underlying motivations or objectives tied to the mission to interpretations of the operational area. In this paper we will define and scope uncertainty, linking it to selected components of decision making. We will discuss the generation of a measure of uncertainty that we are including in simulations along with the supporting information that will impact the decisions. Following this, the selected parameters values for several simulations for multi domain battle scenarios are presented. Analysis and evaluation of the results from these simulations will be shown. Supporting data will be mentioned to frame the results and the plans for future investigations.
Human-Agent teaming requires a fundamental understanding of humans’ interaction with information. The current dynamics of Human Information Interaction (HII) are not fully understood or formalized. The dynamics of current and increasingly, future operations will mandate seamless coupling of humans and automated capabilities. Faster decision making and asymmetric views will critically depend on the performance of these human-agent teams. There are various interactions within the HII field of study including how and why humans find, consume, and use information in order to solve problems, make decisions, and carry out other tasks. There are several parallel between HII and biological interactions; one is the concept of energy. No matter the interaction, energy is acquired and expended. We will focus on one interaction, information consumption. The parallel in biology is consumption to the cellular rate of free energy from the Laws of Thermodynamics in a system at chemical equilibrium. Gibbs Standard Free Energy (ΔG° = − RT ln K) represents the maximum amount of work obtained from a process under conditions of fixed temperature and pressure. This equation can represent the idea of level of work within HII. We mapped variables in the equation to concepts within HII, for example the equilibrium constant (K) links to the balance of information units before and after interaction task. For this research, we are developing an Agent Based Model where complex interaction can be constructed and evaluated. We are using Netlogo, an integrated environment for model development, visualization, and analysis as a tool for developing this model. In this paper, we will present details of the current implementation of our model with the Gibbs Standard Free Energy equation and initial results from the Netlogo simulations of our model.
While the term Internet of Things (IoT) has been coined relatively recently, it has deep roots in multiple other areas of
research including cyber-physical systems, pervasive and ubiquitous computing, embedded systems, mobile ad-hoc
networks, wireless sensor networks, cellular networks, wearable computing, cloud computing, big data analytics, and
intelligent agents. As the Internet of Things, these technologies have created a landscape of diverse heterogeneous
capabilities and protocols that will require adaptive controls to effect linkages and changes that are useful to end users. In
the context of military applications, it will be necessary to integrate disparate IoT devices into a common platform that
necessarily must interoperate with proprietary military protocols, data structures, and systems. In this environment, IoT
devices and data will not be homogeneous and provenance-controlled (i.e. single vendor/source/supplier owned). This
paper presents a discussion of the challenges of integrating varied IoT devices and related software in a military
environment. A review of contemporary commercial IoT protocols is given and as a practical example, a middleware
implementation is proffered that provides transparent interoperability through a proactive message dissemination system.
The implementation is described as a framework through which military applications can integrate and utilize
commercial IoT in conjunction with existing military sensor networks and command and control (C2) systems.