A Decision-Making and Actions Framework for Ball Carriers in American Football Danny Jugan and Dewan T. Ahmed University of North Carolina at Charlotte 9201 University Blvd. Charlotte, NC Abstract that closely matches the decisions, actions, and capabilities of players in an actual football competition. Therefore, in Instructing intelligent agents in team-based, multi-agent en- an effort to maintain the validity of the simulation, behav- vironments to respond to dynamic events is a lengthy and ex- iors that extend beyond normal human ability (e.g., process- pensive undertaking. In this paper, we present a framework for modeling the decisions and behaviors of ball carriers in ing information faster than possible, using data that an ac- American Football using the Axis Football Simulator. While tual player would not have access to, and so forth) will be offensive strategies in football employ the use of prescribed avoided. Additionally, Axis’ rules and regulations (e.g., field plays with specific spatio-temporal goals, players must also size, player capabilities, active participant numbers, and so be able to intelligently respond to the conditions created by forth) are intentionally designed to be consistent with actual their opponent. We utilize a two-part substate framework for football competitions. ball carriers to advance downfield while avoiding defenders. While Axis is running, the user will always control a sin- Unlike existing football simulations that employ variations gle character, leaving 21 other agents to be controlled by on professional football rules and regulations, our method is intelligent scripts. Agents can be categorized into one of demonstrated in a realistic football simulation and produces results that are consistent with actual competitions. a finite number of states based off of their current goal(s). Those goals are established by combining the prescribed instructions of the selected play and dynamic adjustments Introduction made in response to the changing conditions of the environ- When creating simulations or games with the intent to teach, ment (i.e., the states and positions of the surrounding players it is necessary to provide the user with an environment that and the location and possession of the ball). While on of- closely matches the real-world counterpart [1]. This in- fense, an agent will fall into one of four goal-oriented states: cludes not only an appropriate visual representation, but also RUNNING,THROWING,RECEIVING, or BLOCKING. On an accurate resemblance of the decisions and actions of the the defensive side, agents will also be grouped into one of self-governing agents in the environment. In sports simula- four states: ZONE COVERAGE,MAN COVERAGE,BLITZ- tions, additional challenges exist in creating a realistic en- ING, or TACKLING. While this paper will focus only on the vironment due to the added strategy, coordination, and dy- RUNNING framework, we believe the combination of the in- namic nature that is inherent to sporting competitions. dividual methodologies will produce behaviors that closely While there are several published works related to con- resemble the actions of professional football players at both trolling players or overall team decisions — such as coach- the individual and team level. ing responsibilities — in sports simulations, there does not The RUNNING state will function independently as a hier- yet exist a framework for managing the behaviors of players archical state machine divided between positions of behind in American Football (football). The sports-based method- and in front of the imaginary line on the field where the play ologies that do exist are either too generic or not applicable begins (i.e., line of scrimmage). When the player receives to football and are therefore unable to be utilized. Addition- the ball behind the line of scrimmage, a series of raycasts ally, much of the published research pertaining to football will be performed in the surrounding area in order to deter- exists in simulations that do not accurately reflect the rules, mine the best place to run. If the runner crosses the line regulations, or conditions for professional football. of scrimmage, they switch to a different substate that detects The goal of this paper is to present a framework for mod- and avoids nearby defenders. We proposed that this two-part eling the decisions and actions of an offensive ball carrier. framework for controlling offensive ball carriers provides a The methods used in the framework will be implemented us- realistic simulation of decisions and behaviors taken by ac- ing the Axis Football Simulation (Axis) — a 3D American tual players in football competitions. Football simulation created with Unity. Since the simulation can be used as a tool to train football coaches and players, Related Work the overall objective of the agents, under direction of var- In demonstrating proposed models or methodologies for a ious algorithms, is to collectively provide an environment particular set of agents in a sports environment, it is neces- sary to have a simulation in which they can be applied. Rush observes the entire video-based perceptual sequence on ac- 2008, a research extension of Rush 2005, simulates play in tors achieving the intended goal and infers beliefs about each an eight player variant of American football (football) [7]. state. Next, the agent explains how the goal was achieved us- While it was originally developed as a platform for evalu- ing existing knowledge. Finally, the algorithm constructs the ating game-playing agents, several researchers have utilized needed skills along with any supporting knowledge, such as the simulation to produce papers related to modeling or de- specialized start conditions, based on the explanation gen- veloping learning strategies. erated in the previous step. Those skills were tested using Laviers et al. presented an approach for online strat- Rush 2008, and although the level of precision in ICARUS’ egy recognition [6]. Using information about the defense’s control was found to need improvement, the results sug- intent (i.e., play history aggregated from spatio-temporal gested that their method was a viable and efficient approach traces of player movements), their system evaluates the com- to acquiring the complex and structured behaviors required petitive advantage of executing a play switch based on the for realistic agents in modern games. potential of other plays to improve the yardage gained and the similarity of the candidate plays to the current play. A play switch, unlike an audible that changes the play be- fore it starts, makes adjustments to the prescribed move- ments and responsibilities of selected agents after the play has started. Their play switch selection mechanism outper- forms both the built-in Rush offense and a greedy yardage- based switching strategy, increasing yardage while avoiding mis-coordinations induced by the greedy strategy during the transition from the old play to the new one. Laviers and Sukthankar later created a framework for identifying key player agents in Rush 2008 [5]. They dis- covered that in football, like many other multi-agent games, the actions of all the agents are not equally crucial to game- play success. By automatically identifying key players from historical game play, the search space can be focused on player groupings that have the largest impact on yardage gains in a particular formation. Within the Rush football simulator, they observed that each play relied on the success of different subgroups — as defined by the formation — Figure 1: Sample player formation from the Rush 2008 foot- to gain yardage and ultimately touchdowns. They devised ball simulator. a method to automatically identify these subgroups based on the mutual information between the offensive player, While all of these studies produce improvements to mod- defensive blocker, and ball location and the observed ball els or frameworks associated with football strategies, they work flow. Laviers and Sukthankar concluded that they can make no mention of the specific decisions that the individual identify and reuse coordination patterns to focus the search players use while the simulation is running. Additionally, as over the space of multi-agent policies, without exhaustively shown in figure 1, a main problem exists that the simula- searching the set partition of player subgroups. tion in which their strategical improvements are found does Li et al. took a different approach and outlined a system not accurately represent a football environment (e.g., Rush that analyzed preprocessed video footage of human behav- 2008 uses eight players instead of eleven). Removing three ior in a college football game and used it to construct a se- players from each team has a drastic effect on not only the ries of hierarchal skills[7]. The learned skills incorporated strategic planning process, but also on the dynamic spatio- temporal constraints and provided for a variety of coordi- temporal aspects of live play. As stated in a sports-based nated behavior among the players. They used the ICARUS game simulation research report, the development of a pro- architecture as the framework for play observation, execu- gram for playing a sports game presents a problem in that tion, and learning, which is an instance of a unified cognitive the simulation of the game itself must be done in such a way architecture [8]. Since reasoning about the environment is a so that the physical interaction of the game is represented principal task for intelligent agents, ICARUS supplies agents accurately [1]. Some have addressed this problem not by with a combination of low-level perceptual information (i.e., representing the physical performance (i.e., decisions and attributes of a single environmental object) and higher-level actions) of the player, but rather by using statistical data to beliefs(i.e., relations among objects). After inferring that set determine the success or failure of a play based on actual of beliefs about its environment, ICARUS next evaluates its National Football League statistics.