Metareasoning and Social Evaluations in Cognitive Agents

Pinyol, Isaac; Sabater-Mir, Jordi

doi:10.1007/978-3-642-11482-3_15

Cited by 2 publications

(4 citation statements)

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“…1. In the studies that we reviewed, this structure was very common in both non-cooperative MAS [28] and cooperative MAS [1], [5], [6], [9], [17], [20], [24], [26], [29], [30], [35], [36]. Because the meta-levels are independent, no additional communication or coordination between the agents is required, so implementing this structure is easier than implementing a decentralized structure.…”

Section: A Independent Metareasoningmentioning

confidence: 99%

“…Belief updating is part of an agent's object-level reasoning, so it can be controlled by the meta-level if the agent realizes that some agents are sending inaccurate information. Pinyol and Sabater-Mir [28] studied a metareasoning approach that adapted the way that an agent updates its beliefs in order to avoid two extremes (never trusting any agent, and always trusting every agent) that lead to poor system performance.…”

Section: E Belief Updatingmentioning

confidence: 99%

“…For example, Pinyol and Sabater-Mir [28] considered a system in which a buyer agent receives information from multiple informant agents, who provide true and false information about multiple seller agents. The buyer agent modifies the function that it uses to update its beliefs about the sellers in response to the validity of the information that it has already received.…”

Section: B Modifying a Parameter Valuementioning

confidence: 99%

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Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey

et al. 2020

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Autonomous multiagent systems can be used in different domains such as agriculture, search and rescue, and fire protection because they can accomplish large missions more quickly and robustly by dividing them into separate tasks. Using multiple agents introduces additional complexity, which makes autonomous reasoning and decision making more challenging, however. Because agents such as ground robots, unmanned air vehicles, and autonomous underwater vehicles may have limited computational resources, they may need computationally efficient yet powerful reasoning algorithms (decision-making processes that perform deliberation and means-end reasoning). Metareasoning, which is reasoning about these reasoning algorithms, offers a way to tackle these challenges by monitoring and controlling reasoning algorithms to improve agent and system performance. Although metareasoning approaches for individual computational agents have been studied, no survey of metareasoning in multiagent systems (MAS) has yet appeared. This survey fills the existing gap by discussing the multiagent metareasoning approaches that have been studied in the literature. It identifies metareasoning structures, applications of metareasoning to reasoning problems, and the modes (techniques) used to control reasoning processes. This survey contributes to the study of MAS by providing a framework for discussing multiagent metareasoning, highlighting successful approaches, and indicating areas where future work may be fruitful.

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Section: A Independent Metareasoningmentioning

confidence: 99%

Section: E Belief Updatingmentioning

confidence: 99%

Section: B Modifying a Parameter Valuementioning

confidence: 99%

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Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey

et al. 2020

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“…Ideally, one can design metareasoning processes to establish the best function when the system is running in a real scenario. In fact, simple q-learning techniques suffices to some extend for this purpose [34].…”

Section: Relationship Between Image and Reputationmentioning

confidence: 99%

Reputation-based decisions for logic-based cognitive agents

Pinyol

Sabater-Mir

Dellunde

et al. 2010

Auton Agent Multi-Agent Syst

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Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.

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Metareasoning and Social Evaluations in Cognitive Agents

Cited by 2 publications

References 10 publications

Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey

Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey

Reputation-based decisions for logic-based cognitive agents

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