Gav Verma
San Francisco, California, United States
285 followers
273 connections
About
I have a passion for building platforms.
Experience
Education
Publications
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Viewpoints AI
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
This article describes a technical approach to movement-based interactions between a human interactor and an intelligent agent based on the theatrical Viewpoints movement framework. The Viewpoints AI system features procedural gesture interpretation using shallow semantics and deep aesthetics analysis from the Viewpoints framework. The installation creates a liminal virtual / real space for the human and AI to interact by the use of digital projection for the AI visualization and shadow play to…
This article describes a technical approach to movement-based interactions between a human interactor and an intelligent agent based on the theatrical Viewpoints movement framework. The Viewpoints AI system features procedural gesture interpretation using shallow semantics and deep aesthetics analysis from the Viewpoints framework. The installation creates a liminal virtual / real space for the human and AI to interact by the use of digital projection for the AI visualization and shadow play to represent the human. Observations from a recent public demonstration of the system and future directions of work are also discussed.
Other authorsSee publication
Courses
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Advanced Internet Computing
CS 6675
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Advanced Operating Systems
CS 6210
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Artificial Intelligence
CS 6601
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Computability & Algorithms
CS 6505
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Design of Environments
CS 6763
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Expressive AI
CS 8803 Special Topics
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Game AI
CS 8803 Special Topics
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Human Computer Interaction
CS 6750
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Intro to Enterprise Computing
CS 6365
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Machine Learning for Trading
CS 7646
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Mobile Applications and Services
CS 8803 Special Topics
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Software Development Process
CS 6300
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Video Game Design
CS 6457
Projects
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Multi-Objective Optimization for Path Finding
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Autonomous path finding while considering multiple objectives or criteria is a problem that has been tackled by many with varying results. Self-driving vehicles traversing through a desert or hostile environment need to consider not only avoiding uncrossable obstacles but also prioritize their path through different types and levels of terrain. Unmanned Aerial Vehicles (UAV) used for military applications (such as drones) need to consider both the shortest distance to conserve fuel as well as…
Autonomous path finding while considering multiple objectives or criteria is a problem that has been tackled by many with varying results. Self-driving vehicles traversing through a desert or hostile environment need to consider not only avoiding uncrossable obstacles but also prioritize their path through different types and levels of terrain. Unmanned Aerial Vehicles (UAV) used for military applications (such as drones) need to consider both the shortest distance to conserve fuel as well as the maximal proximity from enemies in an adaptive and dynamic manner. Simultaneous optimization of conflicting objectives is a difficult yet very real problem, and the Genetic Algorithm (GA) is a metaheuristic that is well suited for such a class of problems. Simply using weights or a combination of single solutions to balance conflicting objectives is tedious and does not scale well, whereas a decision maker for a Pareto optimal solution with the use of an evolutionary approach such as GA is not only far more practical, but also scales well directly with real-life problems.
Other creators -
Autonomous Path Finding in an Adversarial Environment
This paper proposes a real-time algorithm for guiding an agent through a stochastic and adversarial environment without computing the entire path to the goal node in one step. We incorporate a proximity radius and the Voronoi Diagram coupled with a heuristic based on weighted nodes to avoid adversaries and reach the goal node. Our implementation for a dynamic environment rivals existing algorithms for static environments, with high success ratios found when running simulations against even a…
This paper proposes a real-time algorithm for guiding an agent through a stochastic and adversarial environment without computing the entire path to the goal node in one step. We incorporate a proximity radius and the Voronoi Diagram coupled with a heuristic based on weighted nodes to avoid adversaries and reach the goal node. Our implementation for a dynamic environment rivals existing algorithms for static environments, with high success ratios found when running simulations against even a very high number of adversaries.
Other creators
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