ON THE ROLE OF BDI MODELING FOR INTEGRATED CONTROL AND COORDINATED BEHAVIOR IN AUTONOMOUS AGENTS

This paper describes an architecture for controlling and coordinating autonomous agents, building on previous work addressing reactive and deliberative control methods. The proposed multilayered hybrid architecture allows a rationally bounded, goal-directed agent to reason predictively about potential conflicts by constructing knowledge level models that explain other agents' observed behaviors and hypothesize their beliefs, desires, and intentions; at the same time, it enables the agent to operate autonomously, to react promptly to changes in its real-time environment, and to coordinate its actions effectively with other agents. A principal aim of this research is to understand the role dzfferent functional capabilities play in constraining an agent5 behavior under varying environmental conditions. To this end, an experimental test bed has been constructed comprising a simulated multi-agent world in which a variety of agent configurations and behaviors have been investigated. A number of experimentalfindings are reported.     

Formal specification of beliefs in multi-agent systems

The goal of this paper is to present a logical framework for the formalization of agents' mutual beliefs in a Multi Agent system. The approach is based on a combination of extensional specifications of beliefs and context-based (finite) presentation of the specifications by employing a particular class of Multi Context systems. The extensional specification provides a set-theoretic characterization of beliefs in terms of sets closed under certain conditions. Its finite presentation is provided by using as constructors inference rules inside a Multi Context system. The resulting framework allows for capturing many relevant cases of real (not omniscient) agents, which are very common in Multi Agent scenarios embedded in real world environments. In order to substantiate this claim, two Multi Agent scenarios are formally specified in detail in the specification framework. ©1999 John Wiley & Sons, Inc.     

Learnt Real‐time Meshless Simulation

We present a new real‐time approach to simulate deformable objects using a learnt statistical model to achieve a high degree of realism. Our approach improves upon state‐of‐the‐art interactive shape‐matching meshless simulation methods by not only capturing important nuances of an object's kinematics but also of its dynamic texture variation. We are able to achieve this in an automated pipeline from data capture to simulation. Our system allows for the capture of idiosyncratic characteristics of an object's dynamics which for many simulations (e.g. facial animation) is essential. We allow for the plausible simulation of mechanically complex objects without knowledge of their inner workings. The main idea of our approach is to use a flexible statistical model to achieve a geometrically‐driven simulation that allows for arbitrarily complex yet easily learned deformations while at the same time preserving the desirable properties (stability, speed and memory efficiency) of current shape‐matching simulation systems. The principal advantage of our approach is the ease with which a pseudo‐mechanical model can be learned from 3D scanner data to yield realistic animation. We present examples of non‐trivial biomechanical objects simulated on a desktop machine in real‐time, demonstrating superior realism over current geometrically motivated simulation techniques.     

Animating synthetic dyadic conversations with variations based on context and agent attributes

Conversations between two people are ubiquitous in many inhabited contexts. The kinds of conversations that occur depend on several factors, including the time, the location of the participating agents, the spatial relationship between the agents, and the type of conversation in which they are engaged. The statistical distribution of dyadic conversations among a population of agents will therefore depend on these factors. In addition, the conversation types, flow, and duration will depend on agent attributes such as interpersonal relationships, emotional state, personal priorities, and socio‐cultural proxemics. We present a framework for distributing conversations among virtual embodied agents in a real‐time simulation. To avoid generating actual language dialogues, we express variations in the conversational flow by using behavior trees implementing a set of conversation archetypes. The flow of these behavior trees depends in part on the agents' attributes and progresses based on parametrically estimated transitional probabilities. With the participating agents' state, a ‘smart event’ model steers the interchange to different possible outcomes as it executes. Example behavior trees are developed for two conversation archetypes: buyer–seller negotiations and simple asking–answering; the model can be readily extended to others. Because the conversation archetype is known to participating agents, they can animate their gestures appropriate to their conversational state. The resulting animated conversations demonstrate reasonable variety and variability within the environmental context. Copyright © 2012 John Wiley & Sons, Ltd.     

Distributed control of uncertain multiagent systems for tracking a leader with unknown fractional‐order dynamics

This paper proposes distributed adaptive cooperative control algorithms for second‐order agents to track a leader with unknown dynamics. The models of the followers and the leader are composed of uncertain nonlinear components. The order of the leader's dynamics is unknown and can be fractional. Only the single output information is shared among neighbored agents. To simplify the control design, linearly parameterized neural networks are used to approximate the unknown functions. We first present an adaptive control for leaderless consensus and then extend the method to the tracking problem. Thorough theoretical proofs as well as numerical simulation are included to verify the results. Compared with relevant literature, the new approach applies to a larger variety of systems because (i) knowledge about the structure of leader's model is unnecessary; (ii) the unknown functions in different agents' dynamics can be diverse and arbitrary, in other words, the algorithms apply to heterogeneous agents; (iii) the results can be simply used without parameter calculations.     

Reasoning about Commitments in the Event Calculus: An Approach for Specifying and Executing Protocols

Commitments among agents are widely recognized as an important basis for organizing interactions in multiagent systems. We develop an approach for formally representing and reasoning about commitments in the event calculus. We apply and evaluate this approach in the context of protocols, which represent the interactions allowed among communicating agents. Protocols are essential in applications such as electronic commerce where it is necessary to constrain the behaviors of autonomous agents. Traditional approaches, which model protocols merely in terms of action sequences, limit the flexibility of the agents in executing the protocols. By contrast, by formally representing commitments, we can specify the content of the protocols through the agents' commitments to one another. In representing commitments in the event calculus, we formalize commitment operations and domain-independent reasoning rules as axioms to capture the evolution of commitments. We also provide a means to specify protocol-specific axioms through the agents' actions. These axioms enable agents to reason about their actions explicitly to flexibly accommodate the exceptions and opportunities that may arise at run time. This reasoning is implemented using an event calculus planner that helps determine flexible execution paths that respect the given protocol specifications.     

Cognition-Driven Traffic Simulation for Unstructured Road Networks

Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers, which brings various heterogeneous traffic behaviors. Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation. Most existing traffic methods generate traffic behaviors by adjusting parameters and cannot describe those heterogeneous traffic flows in detail. In this paper, a cognition-driven trafficsimulation method inspired by the theory of cognitive psychology is introduced. We first present a visual-filtering model and a perceptual-information fusion model to describe drivers’ heterogeneous cognitive processes. Then, logistic regression is used to model drivers’ heuristic decision-making processes based on the above cognitive results. Lastly, we apply the high-level cognitive decision-making results to low-level traffic simulation. The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.

     

Crowds by Example

We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.     

Adaptive consensus control of nonlinear systems with unknown control directions

In this paper, we investigate the adaptive consensus control for a class of nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on the backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotic consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme.     

Modelling the effect of landmarks on pedestrian dynamics in urban environments

Landmarks have been identified as relevant and prominent urban elements, explicitly involved in human navigation processes. Despite the understanding accumulated around their functions, landmarks have not been included in simulation models of pedestrian movement in urban environments. In this paper, we describe an Agent-Based Model (ABM) for pedestrian movement simulation that incorporates the role of on-route and distant landmarks in agents' route choice behaviour. Route choice models with and without landmarks were compared by using four scenarios: road distance minimisation, least cumulative angular change, road distance minimisation and landmarks, least cumulative angular change and landmarks. The city centre of London was used as a case study and a set of GPS trajectories was employed to evaluate the model. The introduction of landmarks led to more heterogeneous patterns that diverge from the minimisation models. Landmark-based navigation brought about high pedestrian volumes along the river (up to 13% of agents) and the boundaries of the parks (around 8% of the agents). Moreover, the model evaluation showed that the results of the landmark-based scenarios were not significantly different from the GPS trajectories in terms of cumulative landmarkness, whereas the other scenarios were. This implies that our proposed landmark-based route choice approach was better able to reproduce human navigation. At the street-segment level, the pedestrian volumes emerging from the scenarios were comparable to the trajectories' volumes in most of the case study area; yet, under- and over-estimation were observed along the banks of the rivers and across green areas (up to +7%, −11% of volumes) in the landmark-based scenarios, and along major roads (up to +11% of volumes) in the least cumulative angular change scenario. While our model could be expanded in relation to the agents' cognitive representation of the environment, e.g. by considering other relevant urban elements and accounting for individual spatial knowledge differences, the inclusion of landmarks in route choice models results in more plausible agents that make use of relevant urban information.     

A modeling framework for urban traffic systems microscopic simulation

In this paper a modeling framework for urban traffic systems (UTS) is presented. The model, used for agent based micro-simulation, describes both the traffic network and dynamic entities, namely vehicles, traffic lights, and pedestrians. The framework allows defining systematically the necessary components and their behavior of a model oriented to event driven simulation, which can be executed in a distributed way. In the model, the vehicles are conceived as mobile agents with decision making capabilities that interact with the environment and other entities within the traffic network, performing diverse activities according to numerous situations arisen during the simulation. A multi-level Petri net based formalism, named n-LNS is used for describing the structure of the UTS and the components behavior. The first level describes the traffic network; the second level models the behavior of diverse road network users considered as agents, and the third level specifies detailed procedures performed by the agents, namely travel plans, tasks, etc.     

基于非光滑采样控制算法的二阶有向多智能体系统的一致性

针对二阶有向多智能体系统的一致性问题,在连续时间域和离散时间域分别提出一种非光滑控制协议.首先,提出一种连续时间非光滑一致性协议,通过李雅普诺夫理论和齐次系统理论,证明在无扰动情况下可以实现智能体状态的有限时间一致,而在有扰动情况下智能体状态之间的误差将收敛到一个与控制参数和外部扰动相关的范围内;然后,基于采样控制,提出离散非光滑一致性协议,进一步分析采样周期对智能体状态之间误差的影响,并给出误差收敛区间与控制参数、外部扰动和采样周期关系的显性表达式;最后,通过仿真实例验证理论的正确性和有效性.     

A GPU-assisted hybrid model for real-time crowd simulations

In this paper, we propose two new techniques for real-time crowd simulations; the first one is the clustering of agents on the GPU and the second one is incorporating the global cluster information into the existing microscopic navigation technique. The proposed model combines the agent-based models with macroscopic information (agent clusters) into a single framework. The global cluster information is determined on the GPU, and based on the agents' positions and velocities. Then, this information is used as input for the existing agent-based models (velocity obstacles, rule-based steering and social forces). The proposed hybrid model not only considers the nearby agents but also the distant agent configurations. Our test scenarios indicate that, in very dense circumstances, agents that use the proposed hybrid model navigate the environment with actual speeds closer to their intended speeds (less stuck) than the agents that are using only the agent-based models.     

How Does the Agents' Appearance Affect Users' Interpretation of the Agents' Attitudes: Experimental Investigation on Expressing the Same Artificial Sounds From Agents With Different Appearances

An experimental investigation into how the appearance of an agent such as a robot or PC affects people's interpretations of the agent's attitudes is presented. In general, people are said to create stereotypical agent behavioral models in their minds based on the agents' appearances, and these appearances significantly affect their way of interaction. Therefore, it is quite important to address with the following research question: How does an agent's appearance affect its interactions with people? Specifically, a preliminary experiment was conducted to select eight artificial sounds for which people can estimate two specific primitive attitudes (e.g., positive or negative). Then an experiment was conducted where the participants were presented with the selected artificial sounds through three kinds of agents: a MindStorms robot, AIBO robot, and laptop PC. In particular, the participants were asked to select the correct attitudes based on the sounds expressed by these three agents. The results showed that the participants had better interpretation rates when a PC presented the sounds and lower rates when the MindStorms and AIBO robots presented the sounds, even though the sounds expressed by these agents were the same. The results of this study contribute to the design policy of the interactive agents, such as, What types of appearances should agents have to effectively interact with people, and which kinds of information should these agents express to people?     

Private rank aggregation under local differential privacy

In answer aggregation of crowdsourced data management, rank aggregation aims to combine different agents' answers or preferences over the given alternatives into an aggregate ranking which agrees the most with the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. Existing works that guarantee differential privacy in rank aggregation all assume that the data curator is trusted. In this paper, we formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. By leveraging the approximate rank aggregation algorithm KwikSort , the Randomized Response mechanism, and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort . Theoretical and empirical results show that the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.     

Combining physically-based simulation of colliding objects with trajectory control

This paper describes a method that facilitates the use of physically-based models by animators. The main point is to give the animator a familiar interface, while providing a simulation module which detects collisions, thus enhancing realism. The user gives a set of key-frames to guide motion, but does not have to address problems such as interpenetration avoidance, deformations due to collisions, or realism of motion. The simulator will correct the trajectories and compute deformations according to each object's physical properties (such as mass, inertia, stiffness) as well as the collisions and contacts automatically detected during motion. To achieve this, objects are provided with actuators capable of generating forces and torques computed via generalized proportional-derivative controllers. When deflected by external actions, actuated objects try to return to their initial path. Speed variations over time are computed during the simulation, and depend on the complexity of the paths, on the objects models, and on the events such as collisions occurring during motion. In addition simulations are generated at interactive rates, even in the case of complex articulated objects. This facilitates the fine tuning of an animation sequence.     

Evaluating terminal management performances using SLAM: The case of Athens International Airport

Athens in Greece is the city selected to host the 2004 Olympic Games. Many simulations and analyses have been performed in order to properly approach the logistics problems arising from such an event. In this paper we address one of these problems. More precisely, we present a model for analyzing the terminal of Athens International Airport (AIA) under three different scenarios: (i) a historic scenario, based on a typical “busy day”, (ii) a foreseeable scenario with AIA becoming a hub and increasing its traffic volume, and (iii) a traffic intense scenario, as expected during the 2004 Olympic Games. The airport simulation is performed through the OPAL platform. While the airside analysis does not evidence any major cause of congestion, the landside, evaluated through an enhanced version of the simple landside aggregate model (SLAM), shows possible situations of congestion with a consequent degradation in the level of service provided. The use of SLAM allows signaling out the bottlenecks and the corresponding possible causes. A simple modification in the airport policies is sufficient to significantly improve the overall performance.