Flexible and explainable solutions for multi-agent path finding problems

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. The real-world applications of MAPF require flexibility (e.g., solving variations of MAPF) as well as explainability. In this study, both of these challenges are addressed and some flexible and explainable solutions for MAPF and its variants are introduced

Explanation generation for multi-modal multi-agent path finding with optimal resource utilization using answer set programming

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming

A declarative method for dynamic multi-agent path finding

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents such that no two agents collide with each other. We study a dynamic variant of MAPF, called D-MAPF, which allows changes in the environment (e.g., some existing obstacles may be removed from the environment or moved to some other location, or new obstacles may be included in the environment), and/or changes in the team (e.g., some existing agents may leave and some new agents may join the team) at different times. We introduce a new method to solve D-MAPF, using answer set programming

Multi-modal multi-agent path finding with optimal resource utilization

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other. We study a general version of MAPF, called mMAPF, that involves further challenges, such as multi-modal transportation modes, a set of waypoints to visit for each agent, and consumption of different types of resources. We introduce a declarative method to solve mMAPF, using answer set programming that provides a flexible formal framework to address all these challenges while optimizing multiple objectives

Scalable wi-fi intrusion detection for IoT systems

The pervasive and resource-constrained nature of Internet of Things (IoT) devices makes them attractive to be targeted by different means of cyber threats. There are a vast amount of botnets being deployed every day that aim to increase their presence on the Internet for realizing malicious activities with the help of the compromised interconnected devices. Therefore, monitoring IoT networks using intrusion detection systems is one of the major countermeasures against such threats. In this work, we present a machine learning based Wi-Fi intrusion detection system developed specifically for IoT devices. We show that a single multi-class classifier, which operates on the encrypted data collected from the wireless data link layer, is able to detect the benign traffic and six types of IoT attacks with an overall accuracy of 96.85%. Our model is a scalable one since there is no need to train different classifiers for different IoT devices. We also present an alternative attack classifier that outperforms the attack classification model which has been developed in an existing study using the same dataset