39 citations · 107 across the 10 of their papers we have counts for
7 papers · 1 filter
Acquiring Grounded Representations of Words with Situated Interactive Instruction
Shiwali Mohan, Aaron H. Mininger, James R. Kirk +1
We present an approach for acquiring grounded representations of words from mixed-initiative, situated interactions with a human instructor. The work focuses on the acquisition of…
Analogical Concept Memory for Architectures Implementing the Common Model of Cognition
Shiwali Mohan, Matthew Klenk
Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intel…
Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition
Shiwali Mohan, Matt Klenk, Matthew Shreve +3
Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intel…
Acceptable Planning: Influencing Individual Behavior to Reduce Transportation Energy Expenditure of a City
Shiwali Mohan, Hesham Rakha, Matthew Klenk
Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce COPTER…
A Computational Model for Situated Task Learning with Interactive Instruction
Shiwali Mohan, James Kirk, John Laird
Learning novel tasks is a complex cognitive activity requiring the learner to acquire diverse declarative and procedural knowledge. Prior ACT-R models of acquiring task knowledge f…
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
Shiwali Mohan, Aaron Mininger, John Laird
We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbol…
Relational Reinforcement Learning in Infinite Mario
Shiwali Mohan, John E. Laird
Relational representations in reinforcement learning allow for the use of structural information like the presence of objects and relationships between them in the description of v…