Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

In collaborative privacy preserving planning (CPPP), a group of agents jointly creates a plan to achieve a set of goals while preserving each others' privacy. During planning, agents often reveal the private dependencies between their public actions to other agents, that is, which public action facilitates the preconditions of another public action. Previous work in CPPP does not limit the disclosure of such dependencies. In this paper, we explicitly limit the amount of disclosed dependencies, allowing agents to publish only a part of their private dependencies. We investigate different strategies for deciding which dependencies to publish, and how they affect the ability to find solutions. We evaluate the ability of two solvers -- distribute forward search and centralized planning based on a single-agent projection -- to produce plans under this constraint. Experiments over standard CPPP domains show that the proposed dependency-sharing strategies enable generating plans while sharing only a small fraction of all private dependencies.

This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (designer) who possesses one signal source and aims to control the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the designer, such that the design process avoids the predictions of the agents' strategic selection behaviors. We then introduce the design protocol given a goal of the designer referred to as obedient implementability (OIL) and characterize the OIL in a class of obedient perfect Bayesian Markov Nash equilibria (O-PBME). A new framework for information design is proposed based on an approach of maximizing the optimal slack variables. Finally, we formulate the designer's goal selection problem and characterize it in terms of information design by establishing a relationship between the O-PBME and the Bayesian Markov correlated equilibria, in which we build upon the revelation principle in classic information design in economics. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.

Matching markets are often organized in a multi-stage and decentralized manner. Moreover, participants in real-world matching markets often have uncertain preferences. This article develops a framework for learning optimal strategies in such settings, based on a nonparametric statistical approach and variational analysis. We propose an efficient algorithm, built upon concepts of "lower uncertainty bound" and "calibrated decentralized matching," for maximizing the participants' expected payoff. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. Participants will strategically act in favor of a low uncertainty level to reduce competition and increase expected payoff. We study signaling mechanisms that help to clear the congestion in such decentralized markets and find that the effects of signaling are heterogeneous, showing a dependence on the participants and matching stages. We prove that participants can be better off with multi-stage matching compared to single-stage matching. The deferred acceptance procedure assumes no limit on the number of stages and attains efficiency and fairness but may make some participants worse off than multi-stage matching. We demonstrate aspects of the theoretical predictions through simulations and an experiment using real data from college admissions.

We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities. Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL). We develop an algorithm that practically implements this protocol and employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback. Empirical results demonstrate the strengths of our algorithm: compared to RL baselines, it is more sample-efficient; compared to IL baselines, it achieves competitive success rates while not requiring feedback providers to have agent-specific expertise. We also provide theoretical guarantees of the algorithm under certain assumptions on the teacher and the environment.

Agents operating in unstructured environments often produce negative side effects (NSE), which are difficult to identify at design time. While the agent can learn to mitigate the side effects from human feedback, such feedback is often expensive and the rate of learning is sensitive to the agent's state representation. We examine how humans can assist an agent, beyond providing feedback, and exploit their broader scope of knowledge to mitigate the impacts of NSE. We formulate this problem as a human-agent team with decoupled objectives. The agent optimizes its assigned task, during which its actions may produce NSE. The human shapes the environment through minor reconfiguration actions so as to mitigate the impacts of the agent's side effects, without affecting the agent's ability to complete its assigned task. We present an algorithm to solve this problem and analyze its theoretical properties. Through experiments with human subjects, we assess the willingness of users to perform minor environment modifications to mitigate the impacts of NSE. Empirical evaluation of our approach shows that the proposed framework can successfully mitigate NSE, without affecting the agent's ability to complete its assigned task.

Hindsight rationality is an approach to playing multi-agent, general-sum games that prescribes no-regret learning dynamics and describes jointly rational behavior with mediated equilibria. We explore the space of deviation types in extensive-form games (EFGs) and discover powerful types that are efficient to compute in games with moderate lengths. Specifically, we identify four new types of deviations that subsume previously studied types within a broader class we call partial sequence deviations. Integrating the idea of time selection regret minimization into counterfactual regret minimization (CFR), we introduce the extensive-form regret minimization (EFR) algorithm that is hindsight rational for a general and natural class of deviations in EFGs. We provide instantiations and regret bounds for EFR that correspond to each partial sequence deviation type. In addition, we present a thorough empirical analysis of EFR's performance with different deviation types in common benchmark games. As theory suggests, instantiating EFR with stronger deviations leads to behavior that tends to outperform that of weaker deviations.

The real world is awash with multi-agent problems that require collective action by self-interested agents, from the routing of packets across a computer network to the management of irrigation systems. Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group. Given appropriate mechanisms describing agent interaction, groups may achieve socially beneficial outcomes, even in the face of short-term selfish incentives. In many cases, collective action problems possess an underlying graph structure, whose topology crucially determines the relationship between local decisions and emergent global effects. Such scenarios have received great attention through the lens of network games. However, this abstraction typically collapses important dimensions, such as geometry and time, relevant to the design of mechanisms promoting cooperation. In parallel work, multi-agent deep reinforcement learning has shown great promise in modelling the emergence of self-organized cooperation in complex gridworld domains. Here we apply this paradigm in graph-structured collective action problems. Using multi-agent deep reinforcement learning, we simulate an agent society for a variety of plausible mechanisms, finding clear transitions between different equilibria over time. We define analytic tools inspired by related literatures to measure the social outcomes, and use these to draw conclusions about the efficacy of different environmental interventions. Our methods have implications for mechanism design in both human and artificial agent systems.

Ubiquitous mobile computing have enabled ride-hailing services to collect vast amounts of behavioral data of riders and drivers and optimize supply and demand matching in real time. While these mobility service providers have some degree of control over the market by assigning vehicles to requests, they need to deal with the uncertainty arising from self-interested driver behavior since workers are usually free to drive when they are not assigned tasks. In this work, we formulate the problem of passenger-vehicle matching in a sparsely connected graph and proposed an algorithm to derive an equilibrium policy in a multi-agent environment. Our framework combines value iteration methods to estimate the optimal policy given expected state visitation and policy propagation to compute multi-agent state visitation frequencies. Furthermore, we developed a method to learn the driver's reward function transferable to an environment with significantly different dynamics from training data. We evaluated the robustness to changes in spatio-temporal supply-demand distributions and deterioration in data quality using a real-world taxi trajectory dataset; our approach significantly outperforms several baselines in terms of imitation accuracy. The computational time required to obtain an equilibrium policy shared by all vehicles does not depend on the number of agents, and even on the scale of real-world services, it takes only a few seconds on a single CPU.

Semantic web technologies have shown their effectiveness, especially when it comes to knowledge representation, reasoning, and data integrations. However, the original semantic web vision, whereby machine readable web data could be automatically actioned upon by intelligent software web agents, has yet to be realised. In order to better understand the existing technological challenges and opportunities, in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from that agents community. We start by collating and summarising requirements and core architectural components relating to intelligent software agent. Following on from this, we use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements when it comes to semantic web agent literature. Finally, we propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards, and point to existing work in the broader semantic web community any beyond that could help us to make the semantic web agent vision a reality.