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A message-passing algorithm for multi-agent trajectory planning
TLDR
We describe a novel approach for computing collision-free global trajectories for p agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM). Expand
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The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
TLDR
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. Expand
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Towards efficiently supporting large symbolic declarative memories
TLDR
Efficient access to large declarative memories is one challenge in the development of large-scale cognitive models. Expand
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Extending soar with dissociated symbolic memories
TLDR
We develop a memory-centric analysis of Soar 9, a general cognitive architecture that incorporates multiple long-term memories. Expand
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A Multi-Domain Evaluation of Scaling in a General Episodic Memory
TLDR
Episodic memory endows agents with numerous general cognitive capabilities, such as action modeling and virtual sensing. Expand
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Exploring Reinforcement Learning for Mobile Percussive Collaboration
TLDR
We show that reinforcement learning can incrementally learn percussive beat patterns played by humans and supports real-time collaborative performance in the absence of one or more performers. Expand
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Efficiently Implementing Episodic Memory
TLDR
We present and evaluate formal and empirical results using Soar-EpMem: a task-independent integration of episodic memory with Soar 9, providing a baseline for graph-based, task- independent episodic Memory systems that are practical for real world tasks. Expand
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An Improved Three-Weight Message-Passing Algorithm
TLDR
We describe how the powerful "Divide and Concur" algorithm for constraint satisfaction can be derived as a special case of a message-passing version of the Alternating Direction Method of Multipliers (ADMM) algorithm for convex optimization, and introduce an improved message- passing algorithm based on ADMM/DC. Expand
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Effective and efficient forgetting of learned knowledge in Soar’s working and procedural memories
TLDR
This work evaluates one approach to this problem: forgetting knowledge that is not in active use (as determined by base-level activation) and can likely be reconstructed if it becomes relevant. Expand
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Performance evaluation of declarative memory systems in Soar
TLDR
We evaluate the declarative memories of Soar: working memory, semantic memory, and episodic memory, using a detailed simulation of a mobile robot running for one hour of real time. Expand
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