Action selection and task sequence learning for hybrid dynamical cognitive agents

Aaron, Eric; Admoni, Henny

doi:10.1016/j.robot.2010.05.006

Cited by 8 publications

(19 citation statements)

References 17 publications

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“…• A survey of previously described DI-HDCA learning methods and experimental results in both the Tag game and office environments mentioned above (Aaron and Admoni, 2010;, demonstrating the role of DI-HDCA modeling in adaptive integrated intelligence.…”

Section: Introductionmentioning

confidence: 87%

“…From the perspective of a scientist modeling and analyzing behavior, this mechanism enables the study of connectionism-inspired learning -learning occurs by changing weights of links between elements -with phenomena as low-level as speed and as high-level as intention. [In context, it can also be viewed as a form of reinforcement learning; see Aaron and Admoni (2010).] The integration encoded in DI-HDCA models enables such straightforward learning approaches to be exceptionally effective in DI-HDCAs.…”

Section: Figure 3 | Visualization Of a Full Mode-level System Model Omentioning

confidence: 99%

“…Accordingly, when considering modeling frameworks for the design, development, and deeper understanding of such robots and their behaviors, there are many desired criteria and required constraints for their models. This paper presents one such framework, anchored by dynamical intention modeling (Aaron and Admoni, 2010; to represent cognitive elements underlying goal-directed behavior in embodied robots. With dynamical intention modeling and the accompanying hybrid dynamical cognitive agent (HDCA) framework, essential components that are often treated separately -including reactive and deliberative intelligence, and cognitive and physical behaviors -are unified in a modeling framework that supports high-level behavioral design, low-level cognitive and physical representations, and machine learning methods for integrated, autonomous learning in response to robots' environments.…”

Section: Introductionmentioning

confidence: 99%

“…The continuous evolution of these cognitive activation values is governed by differential equations, with cognitive elements affecting the rates of change in activations of other cognitive elements, as described in sections 2.3 and 2.4. These dynamical cognitive representations can be employed for both low-level reactive intelligence and high-level deliberative planning (Aaron and Admoni, 2010), enabling integration of the two levels.…”

Section: Introductionmentioning

confidence: 99%

“…• A broad description of dynamical intention and HDCA modeling, significantly expanding upon more narrowly focused presentations in Aaron and Admoni (2010) and Aaron et al framework from specific agents, a phrase such as "in this paper" will formulaically be used to indicate specific focus.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Aaron

2016

Front. Robot. AI

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Intelligent embodied robots are integrated systems: as they move continuously through their environments, executing behaviors and carrying out tasks, components for low-level and high-level intelligence are integrated in the robot's cognitive system, and cognitive and physical processes combine to create their behavior. For a modeling framework to enable the design and analysis of such integrated intelligence, the underlying representations in the design of the robot should be dynamically sensitive, capable of reflecting both continuous motion and micro-cognitive influences, while also directly representing the necessary beliefs and intentions for goal-directed behavior. In this paper, a dynamical intention-based modeling framework is presented that satisfies these criteria, along with a hybrid dynamical cognitive agent (HDCA) framework for employing dynamical intentions in embodied agents. This dynamical intention-HDCA (DI-HDCA) modeling framework is a fusion of concepts from spreading activation networks, hybrid dynamical system models, and the BDI (belief-desire-intention) theory of goal-directed reasoning, adapted and employed unconventionally to meet entailments of environment and embodiment. The paper presents two kinds of autonomous agent learning results that demonstrate dynamical intentions and the multi-faceted integration they enable in embodied robots: with a simulated service robot in a grid-world office environment, reactive-level learning minimizes reliance on deliberative-level intelligence, enabling task sequencing and action selection to be distributed over both deliberative and reactive levels; and with a simulated game of Tag, the cognitive-physical integration of an autonomous agent enables the straightforward learning of a user-specified strategy during gameplay, without interruption to the game. In addition, the paper argues that dynamical intentions are consistent with cognitive theory underlying goal-directed behavior, and that DI-HDCA modeling may facilitate the study of emergent behaviors in embodied agents.

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Section: Introductionmentioning

confidence: 87%

Section: Figure 3 | Visualization Of a Full Mode-level System Model Omentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Aaron

2016

Front. Robot. AI

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A Genetic Algorithm-Oriented Model of Agent Persuasion for Multi-agent System Negotiation

Jiménez

Castillo

Márquez

et al. 2020

Agents and Multi-Agent Systems: Technologies and Applications 2020

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Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies

et al. 2011

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Regulation of social exchanges refers to controlling social exchanges between agents so that the balance of exchange values involved in the exchanges are continuously kept-as far as possible-near to equilibrium. Previous work modeled the social exchange regulation problem as a POMDP (Partially Observable Markov Decision Process), and defined the policyToBDIplans algorithm to extract BDI (Beliefs, Desires, Intentions) plans from POMDP models, so that the derived BDI plans can be applied to keep in equilibrium social exchanges performed by BDI agents. The aim of the present paper is to extend that BDI-POMDP agent model for self-regulation of social exchanges with a module, based on HMM (Hidden Markov Model), for recognizing and learning partner agents' social exchange strategies, thus extending its applicability to open societies, where new partner agents can freely appear at any time. For the recognition problem, patterns of refusals of exchange proposals are analyzed, as such refusals are produced by the partner agents. For the learning problem, HMMs are used to capture probabilistic state transition and observation functions that model the social exchange strategy of the partner agent, in order to translate them into POMDP's actionbased state transition and observation functions. The paper formally addresses the problem of translating HMMs into POMDP models and vice versa, introducing the translation algorithms and some examples. A discussion on the results of simulations of strategy-based social exchanges is presented, together with an analysis about related work on social exchanges in multiagent systems.Keywords Social exchange strategy · Recognition and learning of social exchange strategies · Self-regulation of social exchange strategies · Partially observable Markov decision process · Hidden Markov model

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Action selection and task sequence learning for hybrid dynamical cognitive agents

Cited by 8 publications

References 17 publications

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

A Genetic Algorithm-Oriented Model of Agent Persuasion for Multi-agent System Negotiation

Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies

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