This book marries social work and artificial intelligence to provide an introductory guide for using AI for social good. Following an introductory chapter laying out approaches and ethical principles of using AI for social work interventions, the book describes in detail an intervention to increase the spread of HIV information by using algorithms to determine the key individuals in a social network of homeless youth. Other chapters present interdisciplinary collaborations between AI and social work students, including a chatbot for sexual health information and algorithms to determine who is at higher stress among persons with Type 2 Diabetes. For students, academic researchers, industry leaders, and practitioners, these real-life examples from the USC Center for Artificial Intelligence in Society demonstrate how social work and artificial intelligence can be used in tandem for the greater good.

This book marries social work and artificial intelligence to provide an introductory guide for using AI for social good. Following an introductory chapter laying out approaches and ethical principles of using AI for social work interventions, the book describes in detail an intervention to increase the spread of HIV information by using algorithms to determine the key individuals in a social network of homeless youth. Other chapters present interdisciplinary collaborations between AI and social work students, including a chatbot for sexual health information and algorithms to determine who is at higher stress among persons with Type 2 Diabetes. For students, academic researchers, industry leaders, and practitioners, these real-life examples from the USC Center for Artificial Intelligence in Society demonstrate how social work and artificial intelligence can be used in tandem for the greater good.

With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization.

Distributed Sensor Networks is the first book of its kind to examine solutions to this problem using ideas taken from the field of multiagent systems. The field of multiagent systems has itself seen an exponential growth in the past decade, and has developed a variety of techniques for distributed resource allocation. Distributed Sensor Networks contains contributions from leading, international researchers describing a variety of approaches to this problem based on examples of implemented systems taken from a common distributed sensor network application; each approach is motivated, demonstrated and tested by way of a common challenge problem. The book focuses on both practical systems and their theoretical analysis, and is divided into three parts: the first part describes the common sensor network challenge problem; the second part explains the different technical approaches to the common challenge problem; and the third part provides results on the formal analysis of a number of approaches taken to address the challenge problem.

As computer networks (and computational grids) become increasingly complex, the problem of allocating resources within such networks, in a distributed fashion, will become more and more of a design and implementation concern. This is especially true where the allocation involves distributed collections of resources rather than just a single resource, where there are alternative patterns of resources with different levels of utility that can satisfy the desired allocation, and where this allocation process must be done in soft real-time. Distributed Sensor Networks is the first book of its kind to examine solutions to this problem using ideas taken from the field of multiagent systems. The field of multiagent systems has itself seen an exponential growth in the past decade, and has developed a variety of techniques for distributed resource allocation.
Distributed Sensor Networks contains contributions from leading, international researchers describing a variety of approaches to this problem based on examples of implemented systems taken from a common distributed sensor network application; each approach is motivated, demonstrated and tested by way of a common challenge problem. The book focuses on both practical systems and their theoretical analysis, and is divided into three parts: the first part describes the common sensor network challenge problem; the second part explains the different technical approaches to the common challenge problem; and the third part provides results on the formal analysis of a number of approaches taken to address the challenge problem.

This book is the eighth in the successful line of Intelligent Agents books published in LNAI. It is based on the Eighth International Workshop on Agent Theories, Architectures, and Languages, ATAL 2001, held in Seattle, WA, USA, in August 2001.The 31 revised full papers presented together with an overall introduction and two special session overviews were carefully reviewed and selected during two rounds of improvement from 68 submissions. The papers are organized in topical sections on agent modeling; formal specification and verification of agents; agent architectures and languages; agent communication; collaborative planning and resource allocation; trust and safety, formal theories of negotiation; and agents for hand-held, mobile, or embedded devices.

This book constitutes the refereed proceedings of the First International Workshop on Collective Robotics, CRW'98, held as part of the Agents' World 1998 conference in Paris, France, in July 1998.
The 13 revised full papers presented in the book were selected during a vigorous reviewing process. The book brings together research in distributed artificial intelligence and intelligent robotics. Among the topics addressed are multi-agent collaboration, collective learning, self-organization, artificial life, simulation, mobile robots, robot soccer, human-robot cooperation, etc.

This book is based on the second International Workshop on Agent Theories, Architectures, and Languages, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95 in Montreal, Canada in August 1995.
The 26 papers are revised final versions of the workshop presentations selected from a total of 54 submissions; also included is a comprehensive introduction, a detailed bibliography listing 355 relevant publications, and a subject index. The book is structured into seven sections, reflecting the most current major directions in agent-related research. Together with its predecessor, Intelligent Agents, published as volume 890 in the LNAI series, this book provides a timely and comprehensive state-of-the-art report.

This book introduces recent research results for cyber deception, a promising field for proactive cyber defense. The beauty and challenge of cyber deception is that it is an interdisciplinary research field requiring study from techniques and strategies to human aspects. This book covers a wide variety of cyber deception research, including game theory, artificial intelligence, cognitive science, and deception-related technology. Specifically, this book addresses three core elements regarding cyber deception: Understanding human's cognitive behaviors in decoyed network scenarios Developing effective deceptive strategies based on human's behaviors Designing deceptive techniques that supports the enforcement of deceptive strategies The research introduced in this book identifies the scientific challenges, highlights the complexity and inspires the future research of cyber deception. Researchers working in cybersecurity and advanced-level computer science students focused on cybersecurity will find this book useful as a reference. This book also targets professionals working in cybersecurity. Chapter 'Using Amnesia to Detect Credential Database Breaches' and Chapter 'Deceiving ML-Based Friend-or-Foe Identification for Executables' are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Global threats of terrorism, drug-smuggling, and other crimes have led to a significant increase in research on game theory for security. Game theory provides a sound mathematical approach to deploy limited security resources to maximize their effectiveness. A typical approach is to randomize security schedules to avoid predictability, with the randomization using artificial intelligence techniques to take into account the importance of different targets and potential adversary reactions. This book distills the forefront of this research to provide the first and only study of long-term deployed applications of game theory for security for key organizations such as the Los Angeles International Airport police and the U.S. Federal Air Marshals Service. The author and his research group draw from their extensive experience working with security officials to intelligently allocate limited security resources to protect targets, outlining the applications of these algorithms in research and the real world. The book also includes professional perspectives from security experts Erroll G. Southers; Lieutenant Commander Joe DiRenzo III, U.S. Coast Guard; Lieutenant Commander Ben Maule, U.S. Coast Guard; Erik Jensen, U.S. Coast Guard; and Lieutenant Fred S. Bertsch IV, U.S. Coast Guard.

This book constitutes the refereed proceedings of the 7th International Conference on Decision and Game Theory for Security, GameSec 2016, held in New York, NY, USA, in November 2016. The 18 revised full papers presented together with 8 short papers and 5 poster papers were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections on network security; security risks and investments; special track-validating models; decision making for privacy; security games; incentives and cybersecurity mechanisms; and intrusion detection and information limitations in security.

What are the risks of terrorism and what are their consequences and economic impacts? Are we safer from terrorism today than before 9/11? Does the government spend our homeland security funds well? These questions motivated a twelve-year research program of the National Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California, funded by the Department of Homeland Security. This book showcases some of the most important results of this research and offers key insights on how to address the most important security problems of our time. Written for homeland security researchers and practitioners, this book covers a wide range of methodologies and real-world examples of how to reduce terrorism risks, increase the efficient use of homeland security resources, and thereby make better decisions overall.

This book is devoted to the development of efficient algorithms for enhancing security of Multiagent systems deployed in real world. In particular, we present here algorithms developed using the Decision/Game Theoretic frameworks. Our algorithms can be classified into two kinds: First, when the agent has no model of its adversaries, the key idea is to randomize the policy of the agent to minimize the information given out. To that end, we present policy randomization algorithms developed using the MDP/Dec-POMDP frameworks. Second, when the agent has partial model of its adversaries, we model the security domain as a Bayesian Stackelberg game. Given the NP-hardness result to find the optimal solution, we provide efficient MILP based approaches to obtain significant speedups. The technology presented here has initiated and became heart of the ARMOR (Assistant for Randomized Monitoring over Routes) security scheduler, currently deployed at the Los Angeles International Airport (LAX) since August-07. Given the general purpose nature of our algorithms, they can potentially be used for enhancing security at many other major locations such as airports, dams, museums, stadiums and others.

With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization.