Abstract: This research report discusses human group characteristics as a stepping stone to study human-agent team characteristics and dynamics. A human-agent team, or so called actor-agent team (AAT) is a group of humans and agents who interact in a coherent and coordinated way towards a common goal. The concept of AATs relates to actor-agent communities (AACs), as AACs are groups of humans and artificial systems (socio-technical information systems) that intimately work together to achieve a common goal (i.e. solve a problem) (Iacob et al., 2009).
AATs are envisioned to increase human performance in (among others) safety and security domains, emergency management, and traffic control. However, the concept of AATs brings many challenges. Besides the realisation of agents as teammembers, and the realisation of real-world AATs, the interaction between agents and humans is a challenge. If agents are to become (task performing) group members, team membership requires much from agents regarding human-agent interaction. How should agents be designed to become teammembers in an AAT? How can humans best interact with agents? When do trust an agent, or rely on it?
This document discusses human group characteristics to draw implications for AAT dynamics. This document is a follow-up of Gouman et al. (2008) in which stages of team development, group membership and cohesion, subgroups, norms, roles, status, and leadership were discussed. The current report first addresses communication and decision making, after which team performance and implications for AATs are discussed.
Abstract: Integration of UAVs with Air Traffic Control (ATC) is a world wide problem. ATC is already troubled by capacity
problems due to a vast amount of air traffic. In the future when large numbers of Unmanned Aerial Vehicles
(UAVs) will participate in the same airspace, the situation cannot afford to have UAVs that need special attention.
Regulations for UAV flights in civil airspace are still being developed but it is expected that authorities will require
UAVs to operate ‘like manned aircraft’. The implication is that UAVs need to become full participants of a complex
socio-technical environment and need to generate ‘man like’ decisions and behavior. In order to deal with the
complexity a novel approach to developing UAV autonomy is needed, aimed to create an environment that fosters
shared situation awareness between the UAVs, pilots and controllers. The underlying principle is to develop an
understanding of the work domain that can be shared between people and UAVs. A powerful framework to
represent the meaningful structure of the environment is Rasmussen’s abstraction hierarchy. This paper proposes
that autonomous UAVs can base their reasoning, decisions and actions on the abstraction hierarchy framework and
communicate about their goals and intentions with human operators. It is hypothesized that the properties of the
framework can create ‘shared situation awareness’ between the artificial and human operators despite the
differences in their internal workings.
Abstract: The tunnel operator monitors and regulates the flow of traffic inside a tunnel, and takes actions in case an incident occurs. TNO has developed a training simulator that enables the operator to train incident situations. We are currently improving the simulator by developing intelligent agents that support a qualified operator, who trains himself, without the presence of a human instructor. This paper provides an overview of research activities in this project. In particular, it describes two types of educational agents, the authoritative instructor agent and the non-authoritative companion agent.
Abstract: In-vehicle agents can potentially avert dangerous driving situations by adapting to the driver, context and traffic conditions. However, perceptions of system autonomy, the way agents offer assistance, driving contexts and users’ personality traits can all affect acceptance and trust. This paper reports on a survey-based experiment (N=100) that further investigates how these factors affect attitudes. The 2x2, between-subject, video-based design varied driving context (high, low density traffic) and type of agent (providing information, providing instructions). Both type of agent and traffic context affected attitudes towards the agent, with attitudes being most positive towards the instructive agent in a light traffic context. Participants scoring high on locus of control reported a higher intent to follow-up on the agent's instructions. Driving-related anxiety and aggression increased perceived urgency of the video scenario.
Abstract: Traffic state estimation is a prerequisite for traffic surveillance and control. For macroscopic traffic flow models several estimation methods have been investigated, including extended and unscented Kalman filters and particle filters. In
this paper we propose a fuzzy observer for the continuous time version of the macroscopic traffic flow model METANET. In order to design the observer, we first derive a dynamic Takagi-Sugeno fuzzy model that exactly represents the traffic model of a segment of a highway stretch. The fuzzy observer is designed based on the fuzzy model and applied to the traffic model. The simulation results are promising for the future development
of fuzzy observers for a highway stretch or a whole traffic
Abstract: The increasing complexity of our world demands new perspectives on the role of technology in human decision making. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and traffic management, where humans need to engage in close collaborations with artificial systems to observe and understand the situation and respond in a sensible way. The book Interactive Collaborative Information Systems addresses techniques that support humans in situations in which complex information handling is required and that facilitate distributed decision-making. The theme integrates research from information technology, artificial intelligence and human sciences to obtain a multidisciplinary foundation from which innovative actor-agent systems for critical environments can emerge. It emphasizes the importance of building actor-agent communities: close collaborations between human and artificial actors that highlight their complementary capabilities in situations where task distribution is flexible and adaptive. This book focuses on the employment of innovative agent technology, advanced machine learning techniques, and cognition-based interface technology for the use in collaborative decision support systems.
Abstract: Advances in network technologies enable distributed systems, operating in complex physical environments, to co-ordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public safety. In these systems humans and machines will, in close interaction, be adaptive to a changing environment. Various architecture models are proposed for such Networked Adaptive Interactive Hybrid Systems (NAIHS) from different research areas like (networked) sensor fusion, command and control, artificial intelligence, robotics and human machine interaction. In this paper an architecture model is proposed that seeks to combine their merits. The NAIHS model focuses on the ‘hybrid mind’ that is layered in several dimensions
defining specific functional components and their
interactions. Subsequently, the interaction between the human and artificial part of the system is discussed.
Abstract: Correct traffic light phase sequences are critical to efficient traffic flow through road
intersections. If the timing of just one set of lights at a junction is one second from
optimal, the results can lead to major traffic congestion and delays. Currently, traffic
light phases are set using a mixture of experience and empirical techniques to achieve
an optimal phase sequence and, in the worst cases, this process can take many
months. Any technique to speed up the identification of optimal phasings would have
clear benefits to government agencies everywhere.
Thales UK’s Research and Technology Group have investigated how Genetic
Algorithms can solve “real-world” problems and have obtained some promising results
with optimising traffic light phasings. Working with their colleagues at Thales Research
and Technology Netherlands they have developed a demonstration solution based on
the concept of “software agent communities” – that is, groups of software programs
working together for their greater good. This extends the earlier Genetic Algorithms
work by using agent-based technologies to enable the rapid optimisation of larger
traffic light networks, potentially covering an entire town.
Abstract: The proliferation of small mobile devices and wireless networks has resulted in an increasing demand to support the applications found in wired environments on mobile devices. In real time replication systems, such as collaborative systems, this trend gives some new problems to address. The properties of wireless networks are low bandwidth and high latency, which change dynamically over time. The risk of the network getting congested is therefore high with the result that the user will not receive the important information in time. Consequently there is a need to develop algorithms and methods for adaptive work environments and adaptive data distribution, to minimise the traffic load. An architecture based on multi- and mobile-agents is proposed as a solution. Personalized behaviour is included in a flexible and extensible system. A prototype of the architecture has been implemented in a crisis environment and was used for an evaluation. It is assumed that each individual in the field is equipped with a PDA that can communicate with other PDA's in the surrounding and remote servers. Users can report about their environment using a personalized iconic language. Each user is supervised by a personal agent. In case of emergency users are routed outside a dangerous area using a personalised dynamic routing system, called PIRA "Personal Intelligent Routing Assistant". The system and results of testing will be presented in this paper.
Abstract: This paper describes the optimization of traffic light controllers using a model-based reinforcement learning approach. Traffic lights are optimized using mostly local, low-level information, but some high-level information concerning the general traffic situation at neighboring traffic junctions is take into account, enhancing situation awareness and improving decision making. We show, using experiments performed with a traffic simulator, that this approach outperforms existing methods.
Abstract: A tunnel operator monitors and regulates the flow of traffic inside a tunnel. Tunnel operators need to train in a simulator regularly in order to maintain proficiency in handling incident situations. During quiet working hours, the operator has enough time for training. But generally at that time no instructor or colleague operators are present to provide instruction, advises, and feedback. As a solution, we have designed an automated training system. The system employs a conversational agent which supports the operator’s situation assessment tasks. The agent exhibits peer behavior which is unobtrusively directed by didactic strategies. In this paper we present the design, development and application of the agent.