AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society.Comment: To appear, AI Magazin

A Security Game Combining Patrolling and Alarm-Triggered Responses Under Spatial and Detection Uncertainties

Motivated by a number of security applications, among which border patrolling, we study, to the best of our knowledge, the first Security Game model in which patrolling strategies need to be combined with responses to signals raised by an alarm system, which is spatially uncertain (i.e., it is uncertain over the exact location the attack is ongoing) and is affected by false negatives (i.e., the missed detection rate of an attack may be positive). Ours is an infinite-horizon patrolling scenario on a graph, where a single patroller moves. We study the properties of the game model in terms of computational issues and form of the optimal strategies and we provide an approach to solve it. Finally, we provide an experimental analysis of our techniques

The In-Transit Vigilant Covering Tour Problem of Routing Unmanned Ground Vehicles

The routing of unmanned ground vehicles for the surveillance and protection of key installations is modeled as a new variant of the Covering Tour Problem (CTP). The CTP structure provides both the routing and target sensing components of the installation protection problem. Our variant is called the in-transit Vigilant Covering Tour Problem (VCTP) and considers not only the vertex cover but also the additional edge coverage capability of the unmanned ground vehicle while sensing in-transit between vertices. The VCTP is formulated as a Traveling Salesman Problem (TSP) with a dual set covering structure involving vertices and edges. An empirical study compares the performance of the VCTP against the CTP on test problems modified from standard benchmark TSP problems to apply to the VCTP. The VCTP performed generally better with shorter tour lengths but at higher computational cost

Patrolling security games: Definition and algorithms for solving largeinstances with single patroller and single intruder

Security games are gaining significant interest in artificial intelligence. They are characterized by two players (a defender and an attacker) and by a set of targets the defender tries to protect from the attacker\u2bcs intrusions by committing to a strategy. To reach their goals, players use resources such as patrollers and intruders. Security games are Stackelberg games where the appropriate solution concept is the leader\u2013follower equilibrium. Current algorithms for solving these games are applicable when the underlying game is in normal form (i.e., each player has a single decision node). In this paper, we define and study security games with an extensive-form infinite-horizon underlying game, where decision nodes are potentially infinite. We introduce a novel scenario where the attacker can undertake actions during the execution of the defender\u2bcs strategy. We call this new game class patrolling security games (PSGs), since its most prominent application is patrolling environments against intruders. We show that PSGs cannot be reduced to security games studied so far and we highlight their generality in tackling adversarial patrolling on arbitrary graphs. We then design algorithms to solve large instances with single patroller and single intruder

Intelligent deployment strategies for passive underwater sensor networks

Passive underwater sensor networks are often used to monitor a general area of the ocean, a port or military installation, or to detect underwater vehicles near a high value unit at sea, such as a fuel ship or aircraft carrier. Deploying an underwater sensor network across a large area of interest (AOI), for military surveillance purposes, is a significant challenge due to the inherent difficulties posed by the underwater channel in terms of sensing and communications between sensors. Moreover, monetary constraints, arising from the high cost of these sensors and their deployment, limit the number of available sensors. As a result, sensor deployment must be done as efficiently as possible. The objective of this work is to develop a deployment strategy for passive underwater sensors in an area clearance scenario, where there is no apparent target for an adversary to gravitate towards, such as a ship or a port, while considering all factors pertinent to underwater sensor deployment. These factors include sensing range, communications range, monetary costs, link redundancy, range dependence, and probabilistic visitation. A complete treatment of the underwater sensor deployment problem is presented in this work from determining the purpose of the sensor field to physically deploying the sensors. Assuming a field designer is given a suboptimal number of sensors, they must be methodically allocated across an AOI. The Game Theory Field Design (GTFD) model, proposed in this work, is able to accomplish this task by evaluating the acoustic characteristics across the AOI and allocating sensors accordingly. Since GTFD considers only circular sensing coverage regions, an extension is proposed to consider irregularly shaped regions. Sensor deployment locations are planned using a proposed evolutionary approach, called the Underwater Sensor Deployment Evolutionary Algorithm, which utilizes two suitable network topologies, mesh and cluster. The effects of these topologies, and a sensor\u27s communications range, on the sensing capabilities of a sensor field, are also investigated. Lastly, the impact of deployment imprecision on the connectivity of an underwater sensor field, using a mesh topology, is analyzed, for cases where sensor locations after deployment do not exactly coincide with planned sensor locations

Optimizing Distribution Sensor Placement for Border Patrol Interdiction using Microsoft Excel

The purpose of this research was to develop an electronic sensor placement model for border security. A model was developed using Microsoft Excel, with some add-on capabilities, to optimize the placement of electronic sensors on a border network given a pre-determined budgetary constraint. The model is capable of handling multiple sensor types, which are placed together as packages, and allows for daytime, nighttime, or 24 hour operation of each sensor type. Additionally, each sensor can be assigned a specific range and detection probability curve within the given range. The model is capable of optimizing either average coverage, or minimum coverage, across the nodes of a network by selecting the nodes where sensor packages are to be placed. Due to its simplicity and ability to run in Microsoft Excel, it is believed that the model developed in this research can also be used in a number of military applications where border security is necessary

An extended study on addressing defender teamwork while accounting for uncertainty in attacker defender games using iterative Dec-MDPs

Multi-agent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multi-agent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork 1 research in security games. The problem that this paper seeks to solve is the coordination of decentralized defender agents in the presence of uncer-tainty while securing targets against an observing adversary. To address this problem, we offer the following novel contributions in this paper: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column generation that uti-lizes Decentralized Markov Decision Processes (Dec-MDPs) to generate defender strategies that incorporate uncertainty; (iii) New techniques to handle global events (when one or more agents may leave the system) during defender execution; (iv) Heuristics that help scale up in the num-ber of targets and agents to handle real-world scenarios; (v) Exploration of the robustness of randomized pure strategies. The paper opens the door to a potentially new area combining computational game theory and multi-agent teamwork.