Predicting poaching for wildlife Protection

Wildlife species such as tigers and elephants are under the threat of poaching. To combat poaching, conservation agencies (“defenders”) need to (1) anticipate where the poachers are likely to poach and (2) plan effective patrols. We propose an anti-poaching tool CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning), which helps the defenders achieve both goals. CAPTURE builds a novel hierarchical model for poacher-patroller interaction. It considers the patroller’s imperfect detection of signs of poaching, the complex temporal dependencies in the poacher's behaviors and the defender’s lack of knowledge of the number of poachers. Further, CAPTURE uses a new game-theoretic algorithm to compute the optimal patrolling strategies and plan effective patrols. This paper investigates the computational challenges that CAPTURE faces. First, we present a detailed analysis of parameter separation and target abstraction, two novel approaches used by CAPTURE to efficiently learn the parameters in the hierarchical model. Second, we propose two heuristics – piece-wise linear approximation and greedy planning – to speed up the computation of the optimal patrolling strategies. We discuss in this paper the lessons learned from using CAPTURE to analyze real-world poaching data collected over 12 years in Queen Elizabeth National Park in Uganda.  Wildlife poaching presents a significant threat to large-bodied animal species. It is one major driver of the population declines of key wildlife species such as tigers, elephants, and rhinos, which are crucial to the functioning of natural ecosystems as well as local and national economies [1, 2]. Poachers illegally catch wildlife by placing snares or hunting. To combat poaching, both government and non-government agencies send well-trained patrollers to wildlife conservation areas. In this work, we focus on snare poaching. The patrollers conduct patrols with the aim of preventing poachers from poaching animals either by catching the poachers or by removing animal traps set by the poachers. Signs of poaching are collected and recorded during the patrols, including snares, traps and other signs such as poacher tracks, which can be used together with other domain features such as animal density or slope of the terrain to analyze and predict the poachers' behavior [3, 4]. It is critical to learn the poachers' behavior, anticipate where the poachers would go for poaching, and further use such information to guide future patrols and make them more effective. Poachers’ behavior is adaptive to patrols as evidenced by multiple studies [5, 6, 7]. Instead of falling into a static pattern, the distribution of poaching activities can be affected by ranger patrols as the poachers will take the patrol locations into account when making decisions. As a result, the rangers should also consider such dynamics when planning the patrols. Such strategic interaction between the conservation agencies and the poachers make game theory an appropriate framework for the problem. Stackelberg Security Games (SSGs) in computational game theory have been successfully applied to various infrastructure security problems in which the defender