11,029 research outputs found
Towards a Unified View of AI Planning and Reactive Synthesis
International audienceAutomated planning and reactive synthesis are well-established techniques for sequential decision making. In this paper we examine a collection of AI planning problems with temporally extended goals, specified in Linear Temporal Logic (LTL). We characterize these so-called LTL planning problems as two-player games and thereby establish their correspondence to reactive synthesis problems. This unifying view furthers our understanding of the relationship between plan and program synthesis, establishing complexity results for LTL planning tasks. Building on this correspondence, we identify restricted fragments of LTL for which plan synthesis can be realized more efficiently
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different
tasks in an autonomous agent, such as a robot or a virtual entity in a computer
game. BTs are a very efficient way of creating complex systems that are both
modular and reactive. These properties are crucial in many applications, which
has led to the spread of BT from computer game programming to many branches of
AI and Robotics. In this book, we will first give an introduction to BTs, then
we describe how BTs relate to, and in many cases generalize, earlier switching
structures. These ideas are then used as a foundation for a set of efficient
and easy to use design principles. Properties such as safety, robustness, and
efficiency are important for an autonomous system, and we describe a set of
tools for formally analyzing these using a state space description of BTs. With
the new analysis tools, we can formalize the descriptions of how BTs generalize
earlier approaches. We also show the use of BTs in automated planning and
machine learning. Finally, we describe an extended set of tools to capture the
behavior of Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success probabilities
and time to completion
Affect and believability in game characters:a review of the use of affective computing in games
Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions
PRESENCE: A human-inspired architecture for speech-based human-machine interaction
Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system
Engineering Agent Systems for Decision Support
This paper discusses how agent technology can be applied to the design of advanced Information Systems for Decision Support. In particular, it describes the different steps and models that are necessary to engineer Decision Support Systems based on a multiagent architecture. The approach is illustrated by a case study in the traffic management domain
NASA space station automation: AI-based technology review. Executive summary
Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics
Applications of Large Scale Foundation Models for Autonomous Driving
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. Recently
powered by large language models (LLMs), chat systems, such as chatGPT and
PaLM, emerge and rapidly become a promising direction to achieve artificial
general intelligence (AGI) in natural language processing (NLP). There comes a
natural thinking that we could employ these abilities to reformulate autonomous
driving. By combining LLM with foundation models, it is possible to utilize the
human knowledge, commonsense and reasoning to rebuild autonomous driving
systems from the current long-tailed AI dilemma. In this paper, we investigate
the techniques of foundation models and LLMs applied for autonomous driving,
categorized as simulation, world model, data annotation and planning or E2E
solutions etc.Comment: 23 pages. A survey pape
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