The nature of war has changed significantly since the end of World War 2. Battle lines have now become opaque; an attack can come from terrorists who easily blend in as civilians, drones that are undetectable to the eye, or ballistic missiles launched from 500 miles away. To account for the increased lethality of war, a vast budget is required to maintain an active duty army. Simply recruiting a soldier costs $15,000; the cost of treating an injured US solider is about $2 million a year (Bilmes, 2014). We aim to determine whether it is possible to reduce those human costs through the deployment of computational agents and artificial intelligence (AI).
Robots that can understand their context, and deal with novelty in an open world could be a viable solution. In the next big war, AI will be king! Robots that are quicker, stronger, and more accurate will determine who the victor is. Such wars, however, can be unquestionably and unprecedently destructive, maybe to the extent of a global technological meltdown. Nonetheless, such advanced technologies should not fall into the hands of the immoral or the malevolent.
The use of robotics (or other forms of intelligent agents) in warfare has existed since WW2. Many of these early robots (such as the US’s ‘Aphrodite’ drones or the Soviet’s tele-tanks) were either ineffective or only useful for specialised operations. The use of robotics for military operations took off in the 1990s when the MQB-1 Predator drone was used by the Central Intelligence Agency (CIA). Instead of painstakingly controlling drones from close-up radio signals, drones “could be controlled by satellite from any command centre to bridge intelligence gaps” (Gotera, 2003). Although there has been considerable progress in making robots intelligent, autonomous robots still “lack the flexibility to react appropriately in unforeseen situations” (Nitsch, 2013). If autonomous robots are used in the battlefield, soldiers will acquire more responsibilities, not less. Soldiers will be expected to perform normal military tasks and use robotic assets as per mission requirements (Barnes et al., 2009).
Studies show that a soldier’s performance on the battlefield worsens if a second robotic task is added to their inventory. In a simulation experiment conducted by Chen and Joyner (Chen et al., 2009), researchers tested if gunners were able to maintain local security using a semi-autonomous unmanned ground vehicle (UGV). The results indicated that “a gunner’s target detection performance degraded significantly when he or she had to concurrently monitor, manage, or tele-operate a UGV compared to the baseline (gunnery task only)” (Barnes et al., 2009). Researchers tested the ability of participants to identify equipment and personnel while operating a UGV. Authors of the study ran their experiments in a 1/35 scaled Iraqi city. The results similarly indicated that “having an additional semiautonomous robot did not offer any advantages for participants. In some cases, it added to their difficulties”.
These types of studies indicate that having context (also known as situational awareness —SA—in the military) is essential for intelligent computational agents. Defining context for an agent, however, is far more challenging than automating a manual or repetitive task. One of the biggest challenges for developing validated and verified SA is defining objects that are unfamiliar. Often, military operators identified an improvised explosive device (or other type of dangers) only because something didn’t look right. Operators rely on previous experiences to identify subtle cues like inconsistent soil texture or a missing vehicle. If an agent can similarly assist soldiers in identifying dangers via contextual data, robotic agents will be far more effective in the battlefield. We present soldier variables that are considered as conflict contextual constructs, namely: paralinguistic, demographic, visual, and physiological variables.
Paralinguistic variables: Military operations typically occur under noisy and stressful circumstances; speech recognition is highly inaccurate under these conditions. Studies consistently show speech recognition accuracies to drop by 50-60% for speech that is emotional (Rajasekaran et al., 1986). Focusing on acoustic (or paralinguistic) features, however, reduces complexities that commonly plague linguistic features, such as: speaking different languages and dialects, having varying accents, and using different speeds of speech. Some studies indicate that using acoustic variables like speech intensity, pitch, speaking rate, and spectral features (i.e. Mel frequency) are effective at recognising emotions from speech — even under noisy conditions (Tawari et al., 2010).
Demographic variables: Research has shown that accepting robots as partners is enhanced when robots possess human-like characteristics (Kiesler et al., 2008). Examples of soldiers ascribing feelings like empathy towards their robotic tools have already been found. Therefore, it is critical to understand how robotic agents will interact with human teammates in future human-agent teams. Using metrics like personality type, age, and gender, agents could help determine whether a soldier is overreacting to a situation or playing it too safe. Research into using personality tests, demographics, geographical data, and psychographics for context-based purposes is a field that is recently gaining more traction (Batarseh et al., 2018). In a study one of us conducted (Batarseh et al. 2018), we used context-aware user interfaces to build an intelligent emergency application. The study used the Myers-Briggs personality type indication to ‘know’ who the user is and what aspects identify their personality. Individuals with a ‘judger’ personality were provided with a more structured format for the app; ‘perceivers’ were presented with a ‘dynamic’ format and so on (Batarseh et al., 2017). In a military setting, an agent could similarly use personality metrics to personalise its interaction with soldiers.
Visual variables: In real world scenarios, object information is usually degraded due to peripheral vision and distance, shadows, and illumination. Within the computer vision field, a dominant approach towards scene recognition is object-based (Siagian et al., 2005). This approach works well indoors, but for outdoors “spatial simplifications such as flat walls and narrow corridors cannot be assumed” (Siagian et al., 2005). A context-based approach could analyse the scene as a whole and embody a sense of gist. Rather than focusing exclusively on objects, classifying image data as scenes would average out random noise in isolated regions. The structure of an area could be estimated by global image features, rather than relying exclusively on local image structures. Not only would this approach be more computationally efficient, but in certain studies it has also proven to be more accurate (Siagian et al., 2005). For various reasons, analysing image data as scenes is more applicable in military settings than the object-based approach. In the battlefield, video clips of an agent will not always be stable. Agents will have to parse thorough jittery image data (as a result of explosions, gunfire, etc.) and identify the context. Additionally, agents will be expected to quickly identify a scene despite having low spatial frequency.
Physiological variables: An agent can rely on soldiers’ emotions to determine if a situation requires intervention. If a squad of soldiers is clearly distressed, the agent needs to act. Monitoring the mean heart rate, heart rate variability, voice pitch, skin conductance, skin temperature, and respiratory rate are commonly used physiological markers of stress. Most studies attempt to predict the mental stress of working professionals using heart rate data (Wijsman et al., 2011). Few studies have focused on predicting physical and emotional stress in combat or physically involved settings (Kutilek, et al., 2017). Effectively predicting and monitoring the stress levels of a soldier is important to build a context that is relevant to the health of the soldier.
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