PALSY
发表于 2025-3-27 00:49:27
Metareasoning for Multi-agent Epistemic Logicsand agents, we get much of the generality and power of higher-order logic even though we are in a firstorder setting. Finally, we are able to use Athena’s versatile tactics for proof automation in the multi-agent logic. We illustrate by developing a tactic for solving the generalized version of the wise men problem.
Negligible
发表于 2025-3-27 02:39:34
Planning Partially for Situated Agentsh it is situated, (iii) because this environment is highly dynamic. In this paper, we propose a framework to design situated agents capable of planning partially. The framework is based upon the specification of planning problems via an abductive variant of the event calculus.
Spinous-Process
发表于 2025-3-27 08:56:25
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DEFT
发表于 2025-3-27 13:16:13
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Feature
发表于 2025-3-27 16:27:11
https://doi.org/10.1007/978-3-663-13920-1ly implemented in the PROSOCS platform. We also show how, via cycle theories, we can formally verify properties of agents’ behaviour, focusing on the concrete property of agents’ .. Finally, we give some examples to show how different cycle theories give rise to different, heterogeneous agents’ behaviours.
SUGAR
发表于 2025-3-27 19:18:13
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IVORY
发表于 2025-3-27 23:30:47
Wilfried Brauer,Siegfried Münchion primitives other than inform are required. We extend BIT with questions, and discuss the relationship with belief, inform and trust. An answer to a question can lead to trust, when the answer conforms to the beliefs of the agent.
Slit-Lamp
发表于 2025-3-28 02:56:22
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Neutropenia
发表于 2025-3-28 06:30:05
Forschung und Lehre in der Bundesrepublik,bduction is a new form of abduction whose abducibles can be referred to in any search path once abduced. This mechanism is used to propagate information about already-executed actions so that we can modify an alternative plan to accommodate side-effects caused by the already-executed actions.
Affable
发表于 2025-3-28 11:39:03
Literatur zur Wirtschaftsinformatik,agent must replan to maintain rational decision-making. This research uses macro actions to transform the state space for the agent’s decision problem into the desire space of the agent. Reasoning in the desire space, the agent can efficiently maintain rationality in response to addition and removal of goals.