Fusing disparate information within the 4D/RCS architecture

Schlenoff, Craig; Madhavan, Raj; Albus, James S.; Messina, Elena R.; Barbera, Tony; Balakirsky, Stephen

doi:10.1109/icif.2005.1591983

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Figure 2 is an example of a basic AC 3 E construct for collectives of autonomous and semiautonomous systems to work together. The AC 3 E model is a derivative of the 4D/RCS model [5], [6], where this construct further extends and expands cooperative behaviors. There are typically four phases in autonomous planning and control for an individual intelligent mobile platform, (i) goal planning (i.e., resource allocation), (ii) long-range planning, (iii) short-range planning, and (iv) closed-loop control.…”

Section: Proposed Approachmentioning

confidence: 98%

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Belief convergence to facilitate cooperative behaviors

Overstreet

Khorrami

Krishnamurthy

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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In Artificial Intelligence (AI), utility functions are used to compare the relative goodness of an AI system making one decision over another. These utility functions, along with their coefficients and parameters, comprise a set of beliefs. The term "belief" is used to describe how autonomous systems associate quantified confidences in the existence of things that make up their worldly knowledge; hence, decisions made by an AI system are governed by the states of its belief. Just as in control system theory where the states are quantities used to estimate and describe the behaviors of electrical and/or mechanical systems, belief states are quantities that are used to describe and estimate decision making characteristics.For evolving AI systems (e.g., ones that change their belief states over time through learning) and heterogeneous systems (e.g., ones that do not share a common notion of a global belief), it is important to develop mechanisms that will allow these systems to converge their beliefs, where they can determine how each other "thinks." This is important since it will allow each agent in a cooperating collective to make decisions that optimally solves their individual needs, along with their collective needs, without requiring explicit communication or a mediator. In this paper, methodologies are presented that facilitate belief convergence. The significance of this study is that we demonstrate how a state observer, such as a Kalman filter, can be used by each agent in a collective to estimate neighboring belief states. This is done with no a priori information of their neighbors' belief states, and by only comparing each individual's estimate for the required effort of the whole collective to perform a group plan.

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Section: Proposed Approachmentioning

confidence: 98%

“…Examples of this type of architecture can be found in ACT-R [3], [4] and 4D/RCS [5], [6] applications. These architectures are gainfully "fleshed-out" for individual autonomous systems, but these paradigms need further "fleshing-out" to facilitate decentralized cooperation.…”

Section: Motivationmentioning

confidence: 99%

Belief convergence to facilitate cooperative behaviors

Overstreet

Khorrami

Krishnamurthy

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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“…Figure 1 is an example of a basic AC 3 E construct for collectives of autonomous and semi-autonomous systems to work together. The AC 3 E model is a derivative of the 4D/RCS model ( [21], [22]), where this construct further extends and expands cooperative behaviors. There are primarily four phases in autonomous planning and control for an individual intelligent mobile platform, (i) goal assessment, (ii) long-range planning, (iii) short-range planning, and (iv) closed-loop control.…”

Section: Proposed Approachmentioning

confidence: 99%

Decentralized swarming beliefs of distributed autonomous heterogeneous system

Overstreet

Khorrami

Krishnamurthy

2011

Proceedings of the 2011 American Control Conference

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This paper outlines a novel method for Cooperative Behavioral Control of distributed heterogeneous autonomous systems, emulating the methods in which humans collaborate. This method allows autonomous systems to collaborate on tasks and mission goals, similar to how humans interact, and is effective and efficient for real-time resource allocation. The proposed method fundamentally reduces the required communication bandwidths by significantly decreasing the amount of data necessary for real-time information exchange between cooperating agents. This is done by creating a swarm to estimate the beliefs of the collective, and not on physical states which is usually done by classical approaches. In sharing core beliefs, a collective of heterogeneous agents can plan as an individual, inherently and naturally deconflicting the notion of cost and optimality.

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“…Perceptual learning applies to the architectures that actively change the way sensory information is handled or how patterns are learned on-line. This kind of learning is frequently performed to obtain implicit knowledge about the environment, such as spatial maps (RCS [477], AIS [216], MicroPsi [33]), clustering visual features (HTM [292], BECCA [436], Leabra [405]) or finding associations between percepts. The latter can be used within the same sensory modality as in the case of the agent controlled by Novamente engine, which selects a picture of the object it wants to get from a teacher [188].…”

Section: Perceptual Learningmentioning

confidence: 99%

40 years of cognitive architectures: core cognitive abilities and practical applications

2018

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In this paper we present a broad overview of the last 40 years of research on cognitive architectures. To date, the number of existing architectures has reached several hundred, but most of the existing surveys do not reflect this growth and instead focus on a handful of well-established architectures. In this survey we aim to provide a more inclusive and highlevel overview of the research on cognitive architectures. Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning, reasoning and metareasoning. In order to assess the breadth of practical applications of cognitive architectures we present information on over 900 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight the overall trends in the development of the field. In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress.

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Fusing disparate information within the 4D/RCS architecture

Cited by 3 publications

References 17 publications

Belief convergence to facilitate cooperative behaviors

Belief convergence to facilitate cooperative behaviors

Decentralized swarming beliefs of distributed autonomous heterogeneous system

40 years of cognitive architectures: core cognitive abilities and practical applications

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