152,115 research outputs found
Designing Behavior Trees from Goal-Oriented LTLf Formulas
Temporal logic can be used to formally specify autonomous agent goals, but
synthesizing planners that guarantee goal satisfaction can be computationally
prohibitive. This paper shows how to turn goals specified using a subset of
finite trace Linear Temporal Logic (LTL) into a behavior tree (BT) that
guarantees that successful traces satisfy the LTL goal. Useful LTL formulas for
achievement goals can be derived using achievement-oriented task mission
grammars, leading to missions made up of tasks combined using LTL operators.
Constructing BTs from LTL formulas leads to a relaxed behavior synthesis
problem in which a wide range of planners can implement the action nodes in the
BT. Importantly, any successful trace induced by the planners satisfies the
corresponding LTL formula. The usefulness of the approach is demonstrated in
two ways: a) exploring the alignment between two planners and LTL goals, and b)
solving a sequential key-door problem for a Fetch robot.Comment: Accepted as "Most Visionary Paper" in Autonomous Robots and
Multirobot Systems (ARMS) 2023 workshop affiliated with the 22nd
International Conference on Autonomous Agents and Multiagent Systems (AAMAS
2023
A Goal Oriented Navigation System Using Vision
This paper addresses a goal oriented navigation framework in a behavior-based manner for autonomous systems. The framework is mainly designed based on a behavioral architecture and relies on a monocular vision camera to obtain the location of goal. The framework employs a virt ual physic based method to steer the robot towards the goal while avoiding unknown obstacles, located along its path. Simulation results validate the performance of the proposed framework
Applying the MVC design pattern to multi-agent systems
As agent technology becomes more wide-spread, the need for agent-based analysis and design methods and tools will keep growing. An agent, which is an autonomous entity that acts on behalf of the user, has different properties than objects. For example, objects are passive entities that react to external stimuli, but do not exhibit goal directed behavior. On the other hand, agents are active entities that may learn about their environment and react to changes. Because of such crucial differences between objects and agents, object-oriented analysis and design methods cannot accommodate the requirements of engineering agent-based systems. Agents, however, can learn a few things from object-oriented analysis and design. In this paper, we present the Agent Views approach for applying the Model View Controller (MVC) design pattern in designing agent-based systems. This approach will help software developers use a familiar design pattern to determine the types of agents needed to build successful agent-based systems
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms
SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System
Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-01-1-0397); National Science Foundation (IRI 90-24877, SBE-0354378); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-95-1-0657, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2
ABC2 an agenda based multi-agent model for robots control and cooperation
This paper presents a model for the control of autonomous robots that allows cooperation among them. The control structure is based on a general purpose multi-agent architecture
using a hybrid approach made up by two levels. One level is composed of reactive skills capable
of achieving simple actions by their own. The other one uses an agenda used as an opportunistic
planning mechanism to compound, activate and coordinate the basic skills. This agenda handles
actions both from the internal goals of the robot or from other robots. This two level approach allows
the integration of real-time response of reactive systems needed for robot low-level behavior, with a
classical high level planning component that permits a goal oriented behavior. The paper describes
the architecture itself, and its use in three different domains, including real robots, as well as the
issues arising from its adaptation to the RoboCup simulator domai
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