How can we know what stakeholders think and feel about brands in real time and over time? Most brand reputation measures are at the aggregate level (e.g., the Interbrand “Best Global Brands” list) or rely on customer brand perception surveys on a periodical basis (e.g., the Y&R Brand Asset Valuator). To answer this question, brand reputation measures must capture the voice of the stakeholders (not just ratings on brand attributes), reflect important brand events in real time, and connect to a brand’s financial value to the firm. This article develops a new social media–based brand reputation tracker by mining Twitter comments for the world’s top 100 brands using Rust–Zeithaml–Lemon’s value–brand–relationship framework, on a weekly, monthly, and quarterly basis. The article demonstrates that brand reputation can be monitored in real time and longitudinally, managed by leveraging the reciprocal and virtuous relationships between the drivers, and connected to firm financial performance. The resulting measures are housed in an online longitudinal database and may be accessed by brand reputation researchers.

While the concept of gating has been explored in past studies of neural networks, and neural network controllers have been successfully designed through evolutionary computation methods, very little past work has focused on empirically determining the value of adding gating to evolved neural network architectures. In this study, we do precisely that, by examining a neural architecture and genetic representation that explicitly permits the use of gating connections in a neurocontroller, and comparing the evolved controller performance to similar evolved controllers where gating connections are not explicitly included. The performance of these different approaches is evaluated in evolving a neurocontroller for an autonomous agent navigating through a simulated predator-prey environment. We find that the neural architecture that explicitly allows gating clearly outperforms three other architectures without gating, suggesting that there is a clear benefit to having gating connections directed by a command module. Further analysis of the best evolved agent reveals that its controller executes by producing command signals that encode high-level goals, which then modify low-level behaviors to achieve those goals, supporting the hypothesis that allowing gated connections in neural networks substantially improves the neurocontrollers that can be evolved.

This paper presents a team plan specification language that combines work in the creation of generic team plans and design of intelligent interfaces. Two key motivations for developing the language are (1) to combine inter-agent cooperation and operator interaction of complex behaviors into a single plan, and (2) to separate plan design and UI design such that they are created by application domain experts and human interaction experts, respectively. The presented result is a generic language for multi-robot plans that defines tasks to be performed, operator interactions for maintaining situational awareness, and mixed initiative actions to react to operator workload.

The proliferation of unmanned aerial vehicles (UAVs) in civil and military domains has spurred increasingly complex automation design for augmenting operator abilities, reducing workload, and increasing mission effectiveness. We describe the Adaptive Interface Management System (AIMS), an intelligent adaptive delegation interface for controlling and monitoring multiple unmanned vehicles, with a mixed-initiative team model language. A study was conducted to assess understanding of this model language and whether participants exhibited calibrated trust in the intelligent automation. Results showed that operators had accurate memory for role responsibility and were well calibrated to the automation. Adaptive automation design approaches like the one described in this paper can be useful to create mixedinitiative human-robot teams.

Communication is difficult in low income areas, given the lack of land based telecommunication and distances between population centers [1]. New methods to monitor/forecast epidemiological trends will enable our military to execute emerging operational requirements. Hand held devices, such as cell phones, smart phones and personal data assistants (PDAs) provide an effective source for collecting, analyzing and widely disseminating healthcare information, because of their widespread use in the very regions to which our military forces are, and will be, deployed. This effort develops handheld device applications that provide health surveillance, epidemiological analysis and forecasting capabilities.

The proliferation of unmanned aerial vehicles (UAVs) in civil and military domains has spurred increasingly complex automation design for augmenting operator abilities, reducing workload, and increasing mission effectiveness. We describe the Adaptive Interface Management System (AIMS), an intelligent adaptive delegation interface for controlling and monitoring multiple unmanned vehicles, with a mixed-initiative team model language. A study was conducted to assess understanding of this model language and whether participants exhibited calibrated trust in the intelligent automation. Results showed that operators had accurate memory for role responsibility and were well calibrated to the automation. Adaptive automation design approaches like the one described in this paper can be useful to create mixedinitiative human-robot teams.

In recent years, evolutionary computation has been successfully used to solve problems involving engineering design and invention, sometimes producing results that are qualitatively different than previous traditionally-designed solutions. However, while evolutionary methods appear to be a promising tool for supporting design, their usefulness is substantially limited by their computational expense and inability to integrate expert knowledge with evolutionary search. Here we develop and evaluate methods for causally-guided evolutionary design based on expert-supplied cause-effect relations that guide how genetic operators are applied (in contrast to conventional genetic operations which are carried out blindly and randomly), using these methods for antenna array design. To our knowledge, this is the first study that biases genetic operations in response to the specific performance characteristics of the individuals to which they are applied, and the first to use explicit cause-effect relations to guide this process. Our experimental evaluation compares using evolutionary systems with and without causal guidance to design directional dipole antenna arrays that meet pre-specified performance criteria. We find that causally-guided systems produce optimal solutions with significantly greater frequency and significant computational savings, suggesting that this approach may substantially improve the use of evolutionary computation in engineering design.

functional requirements, and produced analytical estimates of the relative eectiveness of three alternative levels of automated assistance to the user. The results suggest that higher levels of automated support will signicantly leverage the span of attention and span of control of the operator, while reducing workload to a manageable level.

The idea of creating a general purpose machine intelligence that captures many of the features of human cognition goes back at least to the earliest days of artificial intelligence and neural computation. In spite of more than a half-century of research on this issue, there is currently no existing approach to machine intelligence that comes close to providing a powerful, general-purpose human-level intelligence. However, substantial progress made during recent years in neural computation, high performance computing, neuroscience and cognitive science suggests that a renewed effort to produce a general purpose and adaptive machine intelligence is timely, likely to yield qualitatively more powerful approaches to machine intelligence than those currently existing, and certain to lead to substantial progress in cognitive science, AI and neural computation. In this report, we outline a conceptual framework for the long-term development of a large-scale machine intelligence that is based on the modular organization, dynamics and plasticity of the human brain. Some basic design principles are presented along with a review of some of the relevant existing knowledge about the neurobiological basis of cognition. Three intermediate-scale prototypes for parts of a larger system are successfully implemented, providing support for the effectiveness of several of the principles in our framework. We conclude that a human-competitive neuromorphic system for machine intelligence is a viable long-term goal, but that for the short term, substantial integration with more standard symbolic methods as well as substantial research will be needed to make this goal achievable.

The analysis and exploration of network data is an important capability in numerous areas of study. To learn about various social phenomena, sociologists study network representations of relationships among individuals called social networks. For example, sociologists can examine these networks in order to determine how tightly knit a group is, or to explore the various social roles in a society [5][11]. Communication patterns can be examined in order to determine which individuals play leadership roles, and to find “bridges”, that is, individuals without whom the network would not be fully connected.

In Part 1 of this report, we outlined a framework for creating an intelligent agent based upon modeling the large-scale functionality of the human brain. Building on those results, we begin Part 2 by specifying the behavioral requirements of a large-scale neurocognitive architecture. The core of our long-term approach remains focused on creating a network of neuromorphic regions that provide the mechanisms needed to meet these requirements. However, for the short term of the next few years, it is likely that optimal results will be obtained by using a hybrid design that also includes symbolic methods from AI/cognitive science and control processes from the field of artificial life. We accordingly propose a three-tiered architecture that integrates these different methods, and describe an ongoing computational study of a prototype “miniRoboscout” based on this architecture. We also examine the implications of some non-standard computational methods for developing a neurocognitive agent. This examination included computational experiments assessing the effectiveness of genetic programming as a design tool for recurrent neural networks for sequence processing, and experiments measuring the speed-up obtained for adaptive neural networks when they are executed on a graphical processing unit (GPU) rather than a conventional CPU. We conclude that the implementation of a large-scale neurocognitive architecture is feasible, and outline a roadmap for achieving this goal.

There has been substantial recent interest in integrating knowledge based reasoning (KBR) and case-based reasoning (CBR) within a single system due to the potential synergisms that could result. Here we describe our recent work investigating the feasibility of a combined KBR-CBR application-independent system for interpreting multi-episode stories/narratives, illustrating it with an application in the domain of interpreting urban warfare stories. A genetic algorithm is used to derive weights for selection of the most relevant past cases. In this setting, we examine the relative value of using input features of a problem for case selection versus using features inferred via KBR, versus both. We find that using both types of features is best (compared to human selection), but that input features are most helpful and inferred features are of marginal value. This finding supports the idea that KBR and CBR provide complimentary rather than redundant information, and hence that their combination in a single system is likely to be useful.

There has been substantial recent interest in integrating knowledge based reasoning (KBR) and case-based reasoning (CBR) within a single system due to the potential synergisms that could result. Here we describe our recent work investigating the feasibility of a combined KBR-CBR application-independent system for interpreting multi-episode stories/narratives, illustrating it with an application in the domain of interpreting urban warfare stories. A genetic algorithm is used to derive weights for selection of the most relevant past cases. In this setting, we examine the relative value of using input features of a problem for case selection versus using features inferred via KBR, versus both. We find that using both types of features is best (compared to human selection), but that input features are most helpful and inferred features are of marginal value. This finding supports the idea that KBR and CBR provide complimentary rather than redundant information, and hence that their combination in a single system is likely to be useful.