Manuela M. Veloso

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Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They(More)
Wepresent a comprehensive survey of robot Learning fromDemonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically,(More)
Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? We present SPIRAL that considers this problem for the performance-critical domain of linear digital signal processing (DSP) transforms. For a specified transform, SPIRAL(More)
Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has focussed on the information management aspects of these systems. But in the past few years, a subfield of DAI focussing on behavior(More)
Vision systems employing region segmentation by color are crucial in real-time mobile robot applications, such as RoboCup[1], or other domains where interaction with humans or a dynamic world is required. Traditionally, systems employing real-time color-based segmentation are either implemented in hardware, or as very specific software systems that take(More)
Planning is a complex reasoning task that is well suited for the study of improving performance and knowledge by learning, i.e. by accumulation and interpretation of planning experience. PRODIGY is an architecture that integrates planning with multiple learning mechanisms. Learning occurs at the planner’s decision points and integration in PRODIGY is(More)
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low communication, but in which they can periodically synchronize in a(More)
Artificial intelligence has progressed to the point where multiple cognitive capabilities are being integrated into computational architectures, such as SOAR, PRODIGY, THEO, and ICARUS. This paper reports on the PRODIGY architecture, describing its planning and problem solving capabilities and touching upon its multiple learning methods. Learning in PRODIGY(More)