Wenji Mao

Social Learning

In recent years, social behavioral data have been exponentially expanding due to the tremendous success of various outlets on the social Web (aka Web 2.0) such as Facebook, Digg, Twitter, Wikipedia, and Delicious. As a result, theres a need for social learning to support the discovery, analysis, and modeling of human social behavioral data. The goal is to discover social intelligence, which encompasses a spectrum of knowledge that characterizes human interaction, communication, and collaborations. The social Web has thus become a fertile ground for machine learning and data mining research. This special issue gathers the state-of-the-art research in social learning and is devoted to exhibiting some of the best representative works in this area.

Cyber-Physical-Social Systems for Command and Control

The article provides a preliminary account of the operational process of command and control based on the cyber-physical-social system (CPSS) and a self-synchronization mechanism. The proposed CPSS for command and control incorporates the essential characteristics of operational mechanism and connects the physical network, cyberspace, mental space, and social network.

Agent-Based Social Simulation and Modeling in Social Computing

Agent-based social simulation (ABSS) as a main computational approach to social simulation has attracted increasing attention in the field of social computing. With the development of computer and information technologies, many new ABSS approaches have been proposed with wide application.. In this paper, we aim at reviewing research and applications of agent-based social simulation and modeling in recent years from a social computing perspective. We identify the underlying social theories for ABSS, its simulation and modeling techniques, and computational frameworks from both individual agent and multi-agent system perspective. We finally address some future research issues in agent-based social simulation and modeling.

Modeling Social Emotions and Social Attributions

INTRODUCTION Emotions play a crucial role in mediating human social relationships (Davidson, Scherer,& Goldsmith, 2003). Whether articulated through body movements, voice, deed, or through the ways we justify our actions, human relationships are laden with emotion. Emotion can act as a signal, communicating information about the senders mental state, indicating his or her future actions, and indirectly inducing emotions in the mind of observers. Emotion can also act as a mental process, altering how people see the world, how they form decisions, and how they respond to the environment. In our work we seek to develop testable computational models that emphasize the relationship between emotion and cognition (Gratch & Marsella, 2001; Marsella & Gratch, 2003). In this chapter, we focus on emotions that have a social component: the rage arising from a perceived offence, the guilt we feel after harming another. Such emotions arise from social explanations involving judgments not only of causality but intention and free will (Shaver, 1985). These explanations underlie how we act on and make sense of the social world. In short, they lie at the heart of social intelligence. With the advance of multi-agent systems, user interfaces, and human-like agents, it is increasingly important to reason about this uniquely human-centric form of social inference. Here we relate recent progress in modeling such socio-emotional judgments. Modeling emotions is a relatively recent focus in artificial intelligence and cognitive modeling and deserves some motivation. Although such models can ideally inform our understanding of human behavior, we see the development of computational models of emotion as a core research focus that will facilitate advances in the large array of computational systems that model, interpret or influence human behavior.

Social Judgment in Multiagent Interactions

Social judgment is a process of social explanation whereby one evaluates which entities deserve credit or blame for multiagent activities. Such explanations are a key aspect of inference in a social environment and a model of this process can advance several design components of multi-agent systems. Social judgment underlies social planning, social learning, natural language pragmatics and computational model of emotion. Based on psychological attribution theory, this paper presents a computational approach to forming social judgment based on an agentýs causal knowledge and communicative interactions with other agents.

Mining actionable behavioral rules

Many applications can benefit from constructing models to predict the behavior of an entity. However, such models do not provide the user with explicit knowledge that can be directly used to influence (restrain or encourage) behavior for the users interest. Undoubtedly, the user often exactly needs such knowledge. This type of knowledge is called actionable knowledge. Actionability is a very important criterion measuring the interestingness of mined patterns. In this paper, to mine such knowledge, we take a first step toward formally defining a new class of data mining problem, named actionable behavioral rule mining. Our definition explicitly states the problem as a search problem in a framework of support and expected utility. We also propose two algorithms for mining such rules. Our experiment shows the validity of our approach, as well as the practical value of our defined problem.

Automatic construction of domain theory for attack planning

Terrorism organizations are devising increasingly sophisticated plans to conduct attacks. The ability of emulating or constructing attack plans by potential terrorists can help us understand the intents and motivation behind terrorism activities. A feasible computational method to construct plans is planning technique in AI. Traditionally, AI planning methods rely on a predefined domain theory which is compiled by domain experts manually. To facilitate domain theory construction and plan generation, we propose a method to construct domain theory automatically from free text data. The effectiveness of our proposed approach is evaluated empirically through experimental studies using real world terrorist plans .

Social causality and responsibility: modeling and evaluation

Intelligent virtual agents are typically embedded in a social environment and must reason about social cause and effect. Social causal reasoning is qualitatively different from physical causal reasoning that underlies most current intelligent systems. Besides physical causality, the assessments of social cause emphasize epistemic variables including intentions, foreknowledge and perceived coercion. Modeling the process and inferences of social causality can enrich the believability and the cognitive capabilities of social intelligent agents. In this paper, we present a general computational model of social causality and responsibility, and empirically evaluate and compare the model with several other approaches.

Evaluating a computational model of social causality and responsibility

Intelligent agents are typically situated in a social environment and must reason about social cause and effect. Such reasoning is qualitatively different from physical causal reasoning that underlies most intelligent systems. Modeling social causal reasoning can enrich the capabilities of multi-agent systems and intelligent user interfaces. In this paper, we empirically evaluate a computational model of social causality and responsibility against human social judgments. Results from our experimental studies show that in general, the models predictions of internal variables and inference process are consistent with human responses, though they also suggest some possible refinement to the computational model.

Social and Economic Computing

Social and economic computing is a cross-disciplinary field focusing on the development of computing technologies that consider social and economic contexts. Social computing and economic computing not only share a number of computing technologies, they also benefit and fertilize each other in computational theories, models, and design. This special issue presents some representative research in social and economic computing from several perspectives.