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Toward Introspective Human Versus Machine Learning of Simulated Airplane Flight Dynamics

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Toward Introspective Human Versus Machine Learning of Simulated Airplane Flight Dynamics Dan Tappan and Matt Hempleman Department of Computer Science Eastern Washington University
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Toward Introspective Human Versus Machine Learning of Simulated Airplane Flight Dynamics Dan Tappan and Matt Hempleman Department of Computer Science Eastern Washington University Abstract This paper presents the preliminary results of an extensible Java architecture for modeling, simulating, visualizing, and analyzing modularized, plug-and-play machine-learning strategies applied to instrument-based airplane flight control. A set of basic flight maneuvers challenged the machine to learn how to fly unsupervised by trial and error, from which the learning module attempted to introspectively determine interdependencies among the many inputs and outputs. For baseline comparison, this work also included a pilot study on human subjects who conducted the same experiments. The overarching goal was to determine how, and how well, both groups learned to solve the same flight-related problems on their own, which could be useful to refine and expand the learning strategies. Introduction Flying an airplane by reference to its cockpit instruments alone no external visual cues is a complex, multidimensional, real-time task that maps a small set of inputs to a large set of dynamically changing outputs in a continuous feedback loop. Formally learning to understand and manipulate such a system is mostly a top-down directed process, whereby a teacher explains problems and how to solve them, and then the learner repeatedly practices variations on the solution process under different conditions until achieving consistent, satisfactory performance (Guralnick and Levy 2009). A problem with this approach for machine learning is that the teacher s investment and oversight may become so extensive that they are almost explicitly programming the solution (Poli, Langdon, and McPhee 2004). Although impractical in real life, learning to fly in a predominantly unsupervised bottom-up manner by trial and error may also be effective. In a simulated environment with no real consequences for failure, the unsupervised learner may be able to develop their own model of how the system operates with far less hands-on involvement from the teacher. Not only may it be possible for this reinforcement approach to achieve the same goals, but if done strategically, it could also introspectively show how it learned to do so for insight into the process of both flying and learning to fly (Haykin 1994; Harrington 2012). This work focuses on an extensible architecture for the modeling, simulation, visualization, and analysis of instrument-based airplane flight control, with a plug-andplay module for the learning strategy. The long-term application is to investigate and compare various machinelearning strategies. This paper describes the architecture, a straightforward proof-of-concept learning strategy, and a pilot study of human subjects for comparison. The primary goal is to determine how, and how well, both groups learn to solve the same flight-related problems on their own. Pedagogical Foundation Any nontrivial system has complex interrelationships among its components. The continuous mapping of inputs to processing to outputs is based on countless direct and indirect dependencies, correlations, causes and effects, stimuli and actions, and so on (Haykin 1994; Jones 2008). The framework for learning here is based on first decomposing the problem space of flight data into its constituent W5H question words (i.e., who, what, when, where, why, and how), and then trying to establish a richly interconnected associative DIKW structure for it hierarchically from superficial to deep understanding as follows (Bloom 1956; Dorn 1989; Irish 1999; Rowley 2007): D ata: raw values with no associativity or context; what questions. I nformation: values in one context; how questions. K nowledge: values in multiple contexts; when, where, and why relationships. W isdom: creation of generalized principles by connecting a network of contexts from different sources for predictive, anticipatory, proactive understanding. Data Information Knowledge Figure 1: Learning Associativity Wisdom An accomplished learner (the who) can generally indicate what happens when and where, and how it happened or how to make it happen, but they do not necessarily understand why. The introspective aspect of this work allows for postanalysis by a subject-matter expert to glean insight into the rationale behind decisions. Such insight could be used to refine teaching and learning processes. System Architecture The system consists of 327 Java classes, with Swing and Java 3D for the graphics. The human test subjects were using this code base primarily for developing an unmanned aerial vehicle simulator as the project in their undergraduate software-engineering course, so much of this code is not directly related to this work yet. The main components of interest here are the flight-dynamics model, machine-learning engine, instrumentation, and data logger. Flight Dynamics The flight dynamics reflect a Cessna 172, which is the world s most popular airplane thanks to its docile handling characteristics and forgiving nature (Cessna 2014). The underlying flight-dynamics model, while a necessary abstraction and simplification of reality, still captures the main elements of any traditional fixed-wing aircraft (FAA 2011). Its six degrees of freedom represent where the airplane is positioned in three-dimensional space, and where it is facing. Specifically, it uses a right-hand coordinate system for x, y, and z, as indicated in Figure 2, where rotation about each axis is respectively roll, pitch, and yaw. yaw y x pitch roll z Figure 2: Coordinate System (Sketchup 2014) In addition, two axes correspond to the main forces of flight. Thrust moves the airplane forward along the x axis, which drag opposes. Lift is always perpendicular to the xy plane, while weight (gravity) is always straight down. The x, y, z and weight components are in the global (world) frame of reference and are independent of the airplane, whereas roll, pitch, yaw, thrust, drag, and lift are in the local frame of reference. Input The flight control surfaces in Figure 3 redirect airflow over the airplane to change the roll, pitch, and yaw, which in turn contribute to changes in the (x, y, z) position. The ele- vator on both sides of the horizontal stabilizer deflects up or down in unison to change pitch. The ailerons outboard on each main wing deflect up or down in opposition to induce roll. The rudder on the vertical stabilizer deflects left or right to coordinate changes in yaw. The flaps inboard on the wings deflect down in unison to increase the wing lift and drag, generally only for landing. Finally, the propeller generates thrust. The Flight Dynamics Processing section describes these relationships in detail. elevator ailerons rudder flaps Figure 3: Flight Control Surfaces (Sketchup 2014) The primary real-world control interface usually involves a wheel, yoke, or stick, as well as pedals. For logistical reasons, the human interface was limited to the keyboard. There were three modes of operation connecting a key press to an action: Instantaneous changes go to the maximum limit immediately and return to neutral upon release. Incremental auto changes occur stepwise until reaching the maximum limit or the key is released, then return stepwise to neutral. Incremental manual changes occur stepwise until reaching the maximum limit or the key is released, then remain there. Opposite action is necessary to neutralize the effect. The throttle was always in incremental manual mode. Otherwise, this paper consider only instantaneous and incremental auto. The modes remained separate in the experiments for independent analysis. The rationale is that instantaneous inputs are likely tied to determining only what the appropriate action is and when, whereas incremental inputs also factor in how much to apply in terms of time, as well as how to cancel the action. Output To fly and especially to learn to fly the pilot needs constant awareness of the state of the airplane with respect to the world, known as situational awareness (FAA 2011). The underlying mathematical model, with its 32 variables, is a major simplification of the real world with perhaps several times this number (Napolitano 2011). However, most of these data are not directly accessible to the pilot, who is limited to observing only what is depicted by the instruments. (Visual and kinesthetic [motion] senses play a role in visual flight, but not in instrument flight; in fact, ignoring kinesthetic inputs, which are dangerously deceiving, is a major challenge.) Excel Instruments depict data or information either by directly presenting it (e.g., altitude determined by air pressure) or indirectly computing it from multiple fused sources (e.g., vertical speed as a change in altitude over time). While the focus on learning here by both human and machine is limited to the instrument depiction, it is valuable (from a DIKW standpoint) to see the underlying raw source. An extensive log file conveniently exports directly to Excel, as in Figure 4. Figure 4: Excel Log Data While these values represent the discrete states of the simulation in every pertinent detail, no human even a subject-matter expert could make intuitive sense of them in this form, which continues for thousands of entries for most maneuvers. Basic visualization as line plots, however, as in Figure 5, can be very revealing. While this representation is beyond the scope of this paper, it is relevant and worthwhile to mention because the key aspect in their value is in deciding which data to plot: meaningful relationships are only apparent when presented as appropriate combinations of independent and dependent variables. Figure 5: Excel Log Plots Humans, lacking any insight into the raw data at all, would not be able to decide wisely which plots to generate. Most combinations would be meaningless, although a human would likely find many baseless correlations. Indeed, in an earlier assignment, students were seriously confused by extraneous data and drew wildly incorrect conclusions. A similar situation commonly occurs with machine learning by overfitting the data, among other causes (Conway 2012). Although a machine can easily consider countless combinations, very few of them would truly reflect meaningful correlative and causative behaviors of the unknown system. Therefore, any brute-force approach on the raw data would need to be selective. This foresight played an important role in deciding how to set up the machine learning to operate on the instrumentation data, as discussed in the Machine Learning section. Instrumentation The nine instruments in Figure 6 depict the refined state of the airplane derived from the raw data. Students in another earlier assignment had already researched their basic form and function, but until this assignment had never seen them in operation. The only difference between the student and machine perspectives was that the students saw this visual representation, whereas the machine saw the equivalent variable representation (e.g., needle position). A. Airspeed Indicator (ASI): shows airspeed in knots. B. Attitude Indicator (AI): shows pitch and roll via an artificial horizon. C. Altimeter: shows altitude in feet above sea level (which is the ground here); the caret, thick needle and thin needle are 10,000, 1,000, and 100 feet, respectively. D. Turn Coordinator (TC): shows rate of turn in degrees per second via the bar, as well as nose-to-tail alignment in a turn via the ball; the Preliminary Results and Discussion section elaborates on this relationship E. Directional Gyro (DG): serves as a compass, where the numbers rotate around the stationary airplane. F. Vertical-Speed Indicator (VSI): shows change in altitude in positive or negative feet per minute. G. Clock: serves as an ordinary clock; the caret and reset button were not in play. H. Tachometer: shows propeller revolutions per minute. I. Stall Warning: shows when the wings have ceased to provide lift, resulting in imminent loss of control. This set of primary instruments, minus G, H, and I, is often called the six pack because together they minimally depict the state of the airplane. Loss of one or more, known as a partial panel, may be accommodated with significantly more difficulty by interpreting the others in combination, but such a condition was not part of this work. Nevertheless, the general approach should still apply, although likely with degraded results. A B D E G H C Flight Dynamics Processing F I Figure 6: Instrument Panel The architecture also supports six navigational instruments, but the panel omitted them for these experiments. None of the tests addressed a global frame of reference that required the pilot to know where the airplane was with respect to the world (except in altitude). 3D Viewer Although the scope of this work was limited to the internal cockpit view of the instruments, for reference after tests, an external view was available. Not only was it entertaining to review both the successful and spectacularly disastrous results, but the discussion proved to be very informative to both students and instructor on why students made their decisions. Such rich reflective and introspective interaction with the machine-learning aspect would be an ideal goal for future work beyond this limited approach. Figure 7 shows three-dimensional visualizations for two attempts at a counterclockwise turn. This visualizer has seen extensive use in the first author s artificial intelligence courses, related pedagogical research, and industry work as a general-purpose world viewer (Tappan 2008, 2009, 2012). The flight-dynamics model is a Java port of the C++ code by Bourg (2002). The main differences are in the input mechanism to account for the instantaneous and incremental modes, the extensive logging capability, and changes to the flight characteristics to model a Cessna 172. Higher-fidelity models are available, but the internals of this one are especially accessible for inspection and logging (Allerton 2009; Napolitano 2011). While the complex differential equations of flight involve countless intricate interactions, the main objectives of this study were to elicit an understanding of at least the following representative cause-and-effect relationships, which are generalized here for aerodynamic reasons beyond the scope of discussion (FAA 2011): An increase in elevator deflection (up) causes an increase in pitch (depicted in the AI), which causes an increase in lift (in the VSI and altimeter) and a decrease in speed (in the ASI) until a stall occurs (in the stall warning); the opposite holds for a decrease in elevator deflection, except for the stall, and the propeller speed increases (in the tachometer). An increase in left aileron deflection (up), and therefore down on the right, causes a roll to the left (in the AI), which causes a turn to the left (in the DG and TC bar and ball), as well as a loss of lift (in the VSI and altimeter); the opposite holds for a decrease in left aileron. An increase in rudder (right) causes a yaw to the right (in the TC ball), which causes a roll to the right (in the AI), which causes a turn to the right (in the DG and TC bar), as well as a loss of lift (in the VSI and altimeter); the opposite holds for a decrease in rudder. The Preliminary Results and Discussion section discusses this relationship further. An increase in flap deflection (down) causes a decrease in pitch (in the AI) and speed (in the ASI), but an increase in lift (in the VSI and altimeter); the opposite is dependent on the initial state. An increase in throttle causes an increase in propeller speed (in the tachometer), which increases thrust (not depicted on any instrument), which results in an increase in speed (in the ASI) and therefore an increase in lift (in the VSI and altimeter); the opposite holds for a decrease in throttle. Machine Learning Figure 7: Turn Visualizations The long-term purpose of this plug-and-play architecture is to investigate various machine-learning strategies applied to this problem space. At this preliminary stage, only a proof-of-concept module is in play. Evaluation of learning (machine and human) was not through the traditional crossvalidation approach of learning on a training set, then performing on a withheld test set. Rather, the goal was simply to reach the objectives however possible reactively, and then for a subject-matter expert to analyze these steps qualitatively to gain insight into how the subjects presumably learned. For now, there is no way to repeat the actions proactively based on this experience, but this capability will be added eventually for rigorous quantitative analysis. Specifically, the steps are: 1. Acquisition: receive data from sensors 2. Transformation: convert data into usable form 3. Fusion: combine data into coherent, unified views 4. Inference: derive unstated data 5. Reasoning: make sense of data 6. Prediction: anticipate trajectory of data It is fair to characterize the provisional approach here as pure brute force and very restrictive, but it does reasonably reflect the students approach of developing their own generalized principles through trial and error without understanding the underlying aerodynamic principles. It is an enumerative approach of trying an input, seeing its effects, and continuing if the trajectory toward the objective appears promising, or discontinuing otherwise and trying something else. The objectives are declarative statements defining the form of an acceptable solution (with some freedom). For humans, English sufficed (e.g., climb at 80 knots); for the machine, it was equivalent hardcoded conditional statements. A priori knowledge was necessary to constrain the solutions to reasonable flight characteristics and avoid undesirable states like flying upside down (Mitchell 1997). Students had acquired this background from earlier research; the machine required additional logic. The reinforcement signal for evaluating trajectory was crude: converging, diverging, or no effect. It functioned somewhat like a myopic feed-forward neural network with no or few hidden layers and a three-state linear activation function (Haykin 1999; Bourg and Seemann 2004). Each of the four inputs (elevator, aileron, rudder, and throttle) mapped to the 11 accessible values in the instruments (roll, pitch, yaw, speed, etc.). Flaps were initially considered but quickly discarded due to their overwhelmingly destructive effect on the other inputs. The direct mapping considered 44 combinations (4 11); the indirect mapping had a second layer with 440 ( ), and a third layer with 3,960 ( ), for a grand total of 4,444 combinations. This network captures relationships of input output, input (output1 output2), and input (output1 output2 output3), respectively. The decreasing count reflects no need to map to the same instrument output twice. This approach addresses steps 1 through 3 above. Experiments A suite of rudimentary experiments provided a rich basis for discovering relationships. Each experiment consisted of a task to perform, which could be attempted any number of times. The logger kept track of the performance data. Tasks The 14 tasks considered are basic flight maneuvers that demonstrate a recognition of the current state of the airplane and some understanding of what needs to be done to achieve the desired next state repeatedly toward the final objective (FAA 2012). Each attempt at satisfying a task started in the air with the same initial conditions and was independent of any others. The attempt ended upon reaching the objective or significantly exceeding the specifications. The tasks could be performed in any order. Straight and level: fly in a straight line with no change in course (0 degrees), altitude (3,000 feet), or speed (80 knots), which are the initial conditions. Indefinite climb:
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