This volume presents novel computational models for representing digital humans and their interactions with other virtual characters and meaningful environments. In this context, we describe efficient algorithms to animate, control, and author human-like agents having their own set of unique capabilities, personalities, and desires. We begin with the lowest level of footstep determination to steer agents in collision-free paths. Steering choices are controlled by navigation in complex environments, including multi-domain planning with dynamically changing situations. Virtual agents are given perceptual capabilities analogous to those of real people, including sound perception, multi-sense attention, and understanding of environment semantics which affect their behavior choices. The roles and impacts of individual attributes, such as memory and personality are explored. The animation challenges of integrating a number of simultaneous behavior and movement demands on an agent are addressed through an open source software system. Finally, the creation of stories and narratives with groups of agents subject to planning and environmental constraints culminates the presentation.

Virtual Crowds: Steps Toward Behavioral Realism

The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience.

Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems.

Large-scale agent-based social simulation is gradually proving to be a versatile methodological approach for studying human societies, which could make contributions from policy making in social science, to distributed artificial intelligence and agent technology in computer science, and to theory and modeling practice in computer simulation systems. Simultaneously, the application areas of largescale agent-based social simulation vary a lot as well, from daily transportation on a city/country level, to large-scale emergency response, prediction of social change, and analysis of social structure. However, current large-scale agent-based social simulation practice is facing difficulties in balancing model complexity and simulation performance. The wide adoption of distributed/parallel mechanism in current large-scale agent-based social simulation has proven to be an efficient solution to achieve system performance and scalability. On the other hand, the trade-off is usually the simplification of the model precision including agent behavior, agent environment and the social networks and interactions, which are proven to be important to understand social phenomena in complex social systems. Based on the existing challenges, this thesis introduces a novel conceptual model for large-scale agent-based social simulation development, gives out the reference implementation of the proposed model components, and presents a simulation study of a case of epidemic prediction and control in the city of Beijing. This conceptual model can be considered as a hybrid model mixing a general agent-based conceptual model and the discrete event simulation paradigm. For the concept of agent in the proposed conceptual model, this thesis presents a new way for implementation. A reference implementation of an agent is constituted by three main parts: (1) agent object, (2) activity pattern, and (3) multi-level decision-making module. With this design, the implemented agents can carry out a lot of complex activities and show diverse behaviors, such as traveling around and joining non-predefined social activities, while staying "simple" and "small" enough for scalability consideration. For the concept of a social network, this thesis presents a new method to generate social networks dynamically for simulating interactions among a group of agents on a large scale during a simulation run. This thesis borrows from the concept of ’social reach’ in a social circle model, and proposes the concept of ’social similarity’ to generate the special type of social networks, friendship. Using the generated entire social network, agents in this model are able to communicate for scheduling joint social activities. When executing joint social activities, a functional entity called ’activity group’ is generated to organize and manage the participants, and a social contact network emerges from the execution. Compared to the concept of agent environments in general ABM conceptual models, the introduced conceptual model separates the concept of an agent environment into physical container, social regulation and functional entity, which overcomes the limitations on environmental completeness in other ABM models and provides flexibility in simulating different system scenarios. The concept of a physical container is introduced to represent the physical environment where agents stay. Typical physical containers are school, classroom, office, bedroom, train, etc. Physical containers are organized hierarchically. Moreover, this concept makes it much easier to include a transportation component in a social simulation model, which is achieved by considering vehicles as movable physical containers in the model. The concept of social regulation, borrowed from the multi-agent system community, is used to model artifacts that can guide and influence agent behavior globally (rules/norms/institutions). With this concept, agents can respond to different situations during a simulation run. For example, regulating agents’ behavior during a disease outbreak is an indispensable part at a large-scale agent-based epidemic simulation. How agents would respond to interventions during a disease outbreak would have a big impact on the model outcomes. The concept of functional entity, borrowed fromthe object-oriented paradigm, is used to model the extra objects in the system that can influence or directly change attributes of either agents, physical containers or social regulations. For example, a disease is modeled as a functional entity to change agents’ healthy status. Temperature can be modeled as a functional entity to change the transmission probability of a disease in a specified location (physical container). Using reference implementations of these concepts, a model of a large-scale artificial city of Beijing is constructed as a case study to test policies for controlling the spread of disease among the full population (19.6 million). This case study can be considered as a proof of concept which exemplifies how large-scale social systems with complex human behavior and social interactions can be modeled with the help of the proposed conceptual model, but still gains reasonable performance. It also indicates potential use in other social science areas, such as microscopic transportation systems and city level evacuation planning.

/pdf/large-scale-agent-based-social-simulation-a-study-on-541pjf17lg.pdf

Large-scale agent-based social simulation: A study on epidemic prediction and control

Coastal water supply infrastructure systems are exposed to saltwater intrusion exacerbated by sea-level rise stressors. To enable assessing the long-term resilience of these systems to the impact of sea-level rise, this study developed a novel hazards-humans-infrastructure nexus framework that enables the integrated modeling of stochastic processes of hazard scenarios, decision-theoretic elements of adaptation planning processes of utility agencies, and dynamic processes of water supply infrastructure performance. Using the proposed framework and data collected from South Miami-Dade service area, a multi-agent simulation model was created to conduct exploratory assessments of the long-term resilience of water supply infrastructure under various sea-level rise scenarios and adaptation approaches. The results showed the capability of the proposed model for scenario landscape generation to discover robust adaptation pathways for enhanced infrastructure resilience under uncertainty. The analysis results could provide actionable scientific information to water infrastructure managers to improve their adaptation planning and investment decision-making processes.

Resilience planning in hazards-humans-infrastructure nexus: A multi-agent simulation for exploratory assessment of coastal water supply infrastructure adaptation to sea-level rise

This work centres on a control framework for general multi-agent systems, which separates the design of agents behaviours the application layer from crosscutting control concerns the control layer regulating message exchange and processing. The goal is to support modelling and execution of a multi-agent system whose evolution is transparently governed by a pluggable control structure. A library of different control structures, including pure-concurrent and time-sensitive real-time and simulation strategies, was developed. The paper describes the developed control framework and focusses on the achievement of control strategies compliant with agent mobility and resource availability. The control design rests on a minimal actor computational model extended with actions, which are independent computational tasks able to execute in parallel. The approach enables model continuity, that is, the same model is used from analysis by simulation to implementation and real-time execution. The framework is prototyped on top of the JADE agent infrastructure. Usability and effectiveness of the resultant approach are demonstrated by a case study based on a complex closed queue network of services. On-going and future work is finally pointed out in the conclusions. Copyright © 2016 John Wiley & Sons, Ltd.

Control centric framework for model continuity in time-dependent multi-agent systems

In this paper we present DIVAs 4.0, a framework that supports the development of large-scale agent-based simulation systems where agents are situated in open environments. DIVAs includes high-level abstractions for the definition of agents and open environments, a micro kernel for the management of the simulation workflow, domain-specific libraries for the rapid development of simulations, and reusable, extendable components for the control and visualization of simulations. We illustrate the use of DIVAs through the development of a simple simulator where virtual agents are situated in a virtual city.

DIVAs 4.0: A Multi-Agent Based Simulation Framework

This paper presents a model for the interaction between context-aware virtual agents and the environment in which they are situated This model applies to multiagent based simulation systems dealing with human-like virtual agents in decentralized, continuous, and dynamic environments The model supports an extensible agent perception module, allowing agents to perceive their environment through multiple senses (sight, hearing, smell, etc) The environment reacts to agent influences as well as user-invoked stimuli by combining these influences to determine the next state of the environment This paper introduces a formalization and an implementation of the model and discusses multiple scenarios involving context-aware virtual agents situated in dynamic environments

Context-Aware Virtual Agents in Open Environments

In order to create realistic simulations,virtual agents need to learn about their environment through perception To date, most multi-agent simulation systems that implement some form of perception have focused heavily on a single sense, vision In this paper we discuss a multi-sense perception system for virtual agents situated in large scale open environments The perception system consists of modules (ie, sensors) for visual, audible and olfactory senses It also includes a perception combination module that combines data received from the multiple sensors into useful knowledge

/pdf/virtual-agent-perception-combination-in-multi-agent-based-57v1ok35vc.pdf

Virtual agent perception combination in multi agent based systems

In this paper we present DIVAs 4.0, a framework that supports the development of large-scale agent-based simulation systems where agents are situated in open environments. DIVAs includes high-level abstractions for the definition of agents and open environments, a microkernel for the management of the simulation workflow, domain-specific libraries for the rapid development of simulations, and reusable, extendable components for the control and visualization of simulations. We illustrate the use of DIVAs through the development of a simulator where virtual agents are situated in a virtual city and an office environment.

DIVAs 4.0: a framework for the development of situated multi-agent based simulation systems

In this paper we discuss a modular multi-sense perception system for DIVAs virtual agents. It is based on the idea that local environmental in???uences are constantly sensed by one or more of an agent’s senses (sight, hearing, smell, etc.). The perception system is extensible and modifiable and allows users to dynamically modify how an agent perceives its environment at execution time. This paper focuses on the modularity of the agent perception architecture rather than the perception algorithms themselves.

Virtual Agent Perception in Multi-agent Based Simulation Systems

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