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29
Distributed intelligence: Overview of the field and its application in multi-robot systems
- Journal of Physical Agents
, 2008
"... Abstract—This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the ..."
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Cited by 37 (1 self)
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Abstract—This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the solution paradigm to be used. We outline three common paradigms for distributed intelligence — the bioinspired paradigm, the organizational and social paradigm, and the knowledge-based, ontological paradigm — and give examples of how these paradigms can be used in multi-robot systems. We then look at a common problem in multirobot systems — that of task allocation — and show how the solution approach to this problem is very different depending upon the paradigm chosen for abstracting the problem. Our conclusion is that the paradigms are not interchangeable, but rather the selection of the appropriate paradigm is dependent
First Steps toward Natural Human-Like HRI
"... Natural human-like human-robot interaction (NHL-HRI) requires the robot to be skilled both at recognizing and producing many subtle human behaviors, often taken for granted by humans. We suggest a rough division of these requirements for NHL-HRI into three classes of properties: (1) social behaviors ..."
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Cited by 29 (21 self)
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Natural human-like human-robot interaction (NHL-HRI) requires the robot to be skilled both at recognizing and producing many subtle human behaviors, often taken for granted by humans. We suggest a rough division of these requirements for NHL-HRI into three classes of properties: (1) social behaviors, (2) goal-oriented cognition, and (3) robust intelligence, and present the novel DIARC architecture for complex affective robots for human-robot interaction, which aims to meet some of those requirements. We briefly describe the functional properties of DIARC and its implementation in our ADE system. Then we report results from human subject evaluations in the laboratory as well as our experiences with the robot
The utility of affect expression in natural language interactions in joint human-robot tasks
- In Proceedings of the 1st ACM International Conference on Human-Robot Interaction
, 2006
"... Recognizing and responding to human affect is important in collaborative tasks in joint human-robot teams. In this paper we present an integrated architecture for HRI and report results from an experiment with this architecture that shows that expressing affect and responding to human affect with af ..."
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Cited by 28 (17 self)
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Recognizing and responding to human affect is important in collaborative tasks in joint human-robot teams. In this paper we present an integrated architecture for HRI and report results from an experiment with this architecture that shows that expressing affect and responding to human affect with affect expressions improves performance in a joint human-robot task. 1.
Aective Recruitment of Distributed Heterogeneous Agents
- In Proc. of 19th National Conference on AI
, 2004
"... Members of multi-robot teams may need to collaborate to accomplish a task due to differences in capabilities. This paper describes an extension of the ALLIANCE architecture that enables agent recruitment within a de-centralized UAV-UGV robot team without task preemp-tion but 1) uses a formal model o ..."
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Cited by 12 (1 self)
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Members of multi-robot teams may need to collaborate to accomplish a task due to differences in capabilities. This paper describes an extension of the ALLIANCE architecture that enables agent recruitment within a de-centralized UAV-UGV robot team without task preemp-tion but 1) uses a formal model of emotions and 2) handles heterogeneity. Affective computing allows re-cruitment to be robust under loss of communication be-tween agents and minimizes the number of messages passed. Data from 66 simulations show that the affec-tive strategy succeeds with a random message loss rate up to 25 % and requires 19.1 % fewer messages to be sent compared to greedy and random, and that of these, affective scales best with team size. Comparisons of broadcast to unicast messaging are also made in simu-lation.
Toward Multimodal Fusion of Affective Cues
, 2006
"... During face to face communication, it has been suggested that as much as 70 % of what people communicate when talking directly with others is through paralanguage involving multiple modalities combined together (e.g. voice tone and volume, body language). In an attempt to render humancomputer intera ..."
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Cited by 8 (1 self)
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During face to face communication, it has been suggested that as much as 70 % of what people communicate when talking directly with others is through paralanguage involving multiple modalities combined together (e.g. voice tone and volume, body language). In an attempt to render humancomputer interaction more similar to human-human communication and enhance its naturalness, research on sensory acquisition and interpretation of single modalities of human expressions have seen ongoing progress over the last decade. These progresses are rendering current research on artificial sensor fusion of multiple modalities an increasingly important research domain in order to reach better accuracy of congruent messages on the one hand, and possibly to be able to detect incongruent messages across multiple modalities (incongruency being itself a message about the nature of the information being conveyed). Accurate interpretation of emotional signals- quintessentially multimodal- would hence particularly benefit from multimodal sensor fusion and interpretation algorithms. In this paper we provide a state of the art multimodal fusion and describe one way to implement a generic framework for multimodal emotion recognition. The system is developed within the MAUI framework [31] and Scherer’s Component Process Theory (CPT) [49, 50, 51, 24, 52], with the goal to be modular and adaptive. We want the designed framework to be able to accept different single and multi modality recognition systems and to automatically adapt the fusion algorithm to find optimal solutions. The system also aims to be adaptive to channel (and system) reliability.
Institutionalization through Reciprocal Habitalization and Typification
- Second NASA Workshop on Radical Agent Concepts (WRAC), NASA Goddard Spaceflight
, 2005
"... When constructing multiagent systems, the designer may approach the system as a collection of individuals or may view the entire system as a whole. In addition to these approaches, it may be beneficial to consider the interactions between the individuals and the whole. Borrowing ideas from the notio ..."
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Cited by 5 (2 self)
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When constructing multiagent systems, the designer may approach the system as a collection of individuals or may view the entire system as a whole. In addition to these approaches, it may be beneficial to consider the interactions between the individuals and the whole. Borrowing ideas from the notion of social construction and building on previous work in synthetic social construction, this paper presents a framework wherein autonomous agents engage in a dialectic relationship with the society of agents around them. In this framework, agents recognize patterns of social activity in their societies, group such patterns into institutions, and form computational representations of those institutions. The paper presents a design framework describing this method of institutionalization, some implementation suggestions, and a discussion of possible applications.
Hydra: A Framework and Algorithms for Mixed-Initiative UAV-Assisted Search and Rescue
"... Abstract—We demonstrate a testbed and algorithms for collaborative human and automated (or mixed-initiative) decision making within the context of outdoor search and rescue. Hydra is a networked simulation tool that allows n human and k automated agents operating under different assumptions to share ..."
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Cited by 5 (0 self)
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Abstract—We demonstrate a testbed and algorithms for collaborative human and automated (or mixed-initiative) decision making within the context of outdoor search and rescue. Hydra is a networked simulation tool that allows n human and k automated agents operating under different assumptions to share control over m unmanned aerial vehicles (UAVs) with cameras, with the goal of locating a hidden subject θ as quickly as possible. The agents are modeled on a pre-defined hierarchy of authority, and the search space is characterized by varying degrees of obstructions. Search is based on iterating the following cycle of four steps: 1) all agents generate image requests based on their individual probability density functions (pdfs), 2) Hydra collects requests and computes an optimal assignment of images to the UAVs, 3) Hydra processes the resulting image data and specifies whether or not the subject was detected, and 4) all agents update their pdfs. We propose initial models and algorithms under this framework, and we show via simulations of a scenario with three agents and one UAV that our method performs 57.7 percent better than a theoretical upper bound for a single agent and UAV. I.
Metaphor of Politics: A Mechanism of Coalition Formation
- In AAAI Workshop: Forming and Maintaining Coalitions and Teams in Adaptive Multiagent Systems
, 2004
"... Hybrid Multi-Agent Architectures support mobile robot colonies moving in dynamic, unpredictable and time vary-ing environments to achieve collective team-oriented behav-iors for solving complicate and difficult tasks. The develop-ment of a new coalition formation and coordination frame-work for robo ..."
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Cited by 4 (1 self)
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Hybrid Multi-Agent Architectures support mobile robot colonies moving in dynamic, unpredictable and time vary-ing environments to achieve collective team-oriented behav-iors for solving complicate and difficult tasks. The develop-ment of a new coalition formation and coordination frame-work for robot colonies in dangerous, unknown and dynamic environment is outlined. The name of this new framework is Metaphor of Politics (MP), and it loosely takes inspiration from the political organizations of democratic governments. The main characteristic of the proposed framework lies in its dynamic reconfigurability in order to adapt the robot colony to environmental changes.
The roles of the amygdala in the affective regulation of body, brain, and behaviour
- Connection Science
, 2010
"... Abstract. Despite the great amount of knowledge produced by the neu-roscientific literature affective phenomena, current models tackling non-cognitive aspects of behavior are often bio-inspired but rarely bio-constrained. This paper presents a theoretical account of affective systems centered on the ..."
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Cited by 4 (3 self)
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Abstract. Despite the great amount of knowledge produced by the neu-roscientific literature affective phenomena, current models tackling non-cognitive aspects of behavior are often bio-inspired but rarely bio-constrained. This paper presents a theoretical account of affective systems centered on the amygdala. This account aims to furnish a general framework and spe-cific pathways to implement models that are more closely related to bi-ological evidence. The amygdala, which receives input from brain areas encoding internal states, innately relevant stimuli, and innately neutral stimuli, plays a fundamental role inmotivational and emotional processes of organisms. This role is based on the fact that amygdala implements the two associative processes at the core of Pavlovian learning (CS-US and CS-UR associations), and that it has the capacity of modulating these as-sociations on the basis of internal states. These functionalities allow the amygdala to play an important role in the regulation of the three fun-damental classes of affective responses (namely, the regulation of body states, the regulation of brain states via neuromodulators, and the trig-gering of a number of basic behaviours fundamental for adaptation) and in the regulation of three high-level cognitive processes (namely, the af-fective labeling of memories, the production of goal-directed behaviours, and the performance of planning and complex decision making). Our analysis is conducted within a methodological approach that stresses the importance of understanding the brain within an evolutionary/adaptive framework and with the aim of isolating general principles that can po-tentially account for the wider possible empirical evidence in a coherent fashion. 1
Affective Goal and Task Selection for Social Robots
- Handbook of Research on Synthetic Emotions and Sociable Robotics
, 2009
"... Effective decision-making under real-world conditions can be very difficult. From a purely decision-theoretic standpoint, the optimal way of making decisions – rational choice – requires an agent to know the utilities of all choice options as well as their associated likelihoods of succeeding for th ..."
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Cited by 3 (0 self)
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Effective decision-making under real-world conditions can be very difficult. From a purely decision-theoretic standpoint, the optimal way of making decisions – rational choice – requires an agent to know the utilities of all choice options as well as their associated likelihoods of succeeding for the agent to be able to calculate the expected utility of each alternative and being able to select the one with the maximum utility. Unfortunately, such rational methods are in practice often not applicable (e.g., because the agent does not have reliable or sufficient knowledge) or feasible (e.g., because it is too time-consuming to perform all necessary calculations). Psychologists have long hypothesized that humans are able to cope with time, knowledge and other resource limitations by employing affective evaluations (Clore, Gasper, & Conway, 2001) rather than rational ones. For affect provides fast, low-cost (although often less accurate) mecha-nisms for estimating the value of an object, event, or situation for an agent, as opposed to longer, more complex and more computationally intensive cognitive evaluations (e.g., to compute the ex-pected utilities) (Kahneman, Wakker, & Sarin, 1997). Humans also rely on affective memory, which seems to encode implicit knowledge about the likelihood of occurrence of a positive or negative future event (Blaney, 1986). Finally, affect also influences human problem-solving and
