事件演算在行动推理中的应用

事件演算是基于一阶谓词演算的行动推理理论.它可作为描述事件的一个工具,在行动推理的应用中显示出其强大的表示能力和实现能力.在事件演算中,可以对行动进行公理化,可以描述行动的时间性、并发性、连续变化及知识,而且还可用Prolog实现.讨论介绍与这些应用相关的基本概念、思想和方法等,并且通过一个送咖啡的例子说明了如何通过事件演算来描述和实现.     

传感器机器人行动推理的有色网表示模型

研究机器人行动推理优化系统,针对传统的推理前必须预先给定所有环境状态,不能动态获取环境状态新知识.为了使得机器人在推理的过程中能动态获取环境状态新知识以提高推理的准确度,提出了行动推理过程中的两种基本动作即外部动作和感知动作进行了形式化地表示,对这两种基本动作、STRIPS 推理规则以和有色网来表示机器人在不完全可知环境下进行行动推理的形式化表示,采用 PNS (Petri Net for Reasoning about Action with sensor)网系统,采用 CPNT<,ools>对办公环境下机器人行动推理实验,结果表明 PNS 网系统能使得机器人在行动推理过程中动态获取新知识目标,提高了行动推理的准确度.     

自主Agent结构AASC及其原型支撑系统AASS

介绍了一个能刻画agent的多种特征,尤其是自主性的自主agent结构AASC。此结构结合了BDI结构和情境演算的优点,既能表示agent的信念、目标、策略等心智成分,又能进行行动推理和规划。为了能够方便用户建造自主agent,基于自主agent结构AASC,开发了AASC的原型支撑系统AASS。介绍了AASS的总体结构,讨论了AASS的主要成分类,说明了如何利用该系统开发agent应用系统。     

面向行动的信念更新

基于可能模型方法 ( possible m odel approach,简称 PMA) ,提出了面向行动的信念更新的概念 ,证明了在信息完备的情境演算系统中 ,一个一阶公式在情境 s下成立当且仅当它属于情境 s下的信念集 .这一结果为有效避免情境演算推理中二阶归纳公理的使用提供了一条可行的途径 ,也为基于意向驱动的 agent模型的建立以及面向 agent的程序设计语言 AOPL ID( agent-oriented programm ing language with intention driver)的提出提供了必要的理论基础.     

基于流演算的智能体行动推理模型设计与实现

并发约束程序设计在人工智能程序设计领域中占据越来越重要的位置,约束处理规则作为新一代的并发程序设计正倍受关注.对约束处理规则和流演算理论及其实现语言FLUX进行了研究,结合流演算和JCHR推理模型优点,设计了一种基于Java的流演算解释器JFLUX,同时提出了一个基于目标驱动的,在不完全可知的虚拟环境中通过感知到的有限信息进行自主行动推理能力的智能体模型,实现了办公室场景中智能体行动推理系统.     

基于流演算的智能虚拟人模型研究与实现*

在研究流演算理论及其实现语言FLUX的基础上,将流演算与虚拟现实技术中的虚拟人相结合,提出了一个基于目标驱动的、有自主行动能力的虚拟人模型。设计了动作检测模块,同时使用了动作队列,根据动作检测的结果来决定是否执行下一个动作,使虚拟人可以针对动态变化的虚拟环境进行有效的行动规划。利用此模型可以快速构建出一个在不完全可知的虚拟环境中通过感知到的有限信息进行实时的、自主行动推理的智能虚拟人。最后,实现了办公室场景中智能虚拟人行动推理系统。     

基于情境演算的智能体结构

Agent结构的建立是Agent研究的重要内容.尝试着结合BDI结构和情境演算的优点,提出了一个能够刻画Agent的多种特征,尤其是自主性的智能体结构AASC(Agent architecture based on situation calculus).此结构既能表示Agent的信念、目标、策略等心智状态,又能进行行动推理和规划,为解释Agent的自主性、建构不同类型的Agent提供了统一的平台.     

时序Agent程序设计语言TAOPLID的离线解释器

AOPLID是一种面向agent程序设计语言。本文旨在对AOPLID语言进行时序扩充,使之能表达并处理带时间参数的并发行动,基于离线方式下AOPLID程序的语义,用Prolog语言实现时序AOPLID语言(TAOPLID)的离线解释嚣。首先,我们对经典情境演算进行适当改造,使之能描述合时间变元的行动,因为持续行动一般可认为是具有瞬时开始行动和瞬时终止行动的过程,所以可以将一个持续动作分解为两个时间上互不相交的瞬时动作,再引入一个新的关系流刻画这两个瞬时动作的执行情况,从而可在扩充后的情境演算中表达带时间参数的并发行动。其次,为使TAOPLID离线解释嚣方便处理以集合方式表示的TAOPLID程序,设计并实现了TAOPLID预处理嚣,它将TAO—PLID程序的集合形式转换成Prolog子句形式,然后通过TAPOLID离线解释嚣对其解释生成一可执行的原子行动序列。     

行动推理中若干问题的研究

1 引言我们所面临的世界是不断动态变化的,一个智能系统往往需要对动态变化的环境做出反应,其中一个重要方面是对各种行动的结果进行预测、推理,以决定下一步的目标和动作。John McCarthy提出进行行动推理(Reasoning about action)研究,并认为行动推理在常识推理中占有基础性的地位。至此以后,行动推理成为人工智能的一项重要研究内容。利用形式化的方法对世界和行动进行描述和推理构成了行动推理的主要内容。行动推理有时也被称为行动逻辑。在这里我们把关于行动和变化的推理总称为行动推理。行动推     

支持设计环境中学习的多Agent系统

设计是工业生产过程中最能体现人的智能并决定产品性能和成本的重要阶段 .学习可以有效地利用经验知识改进设计者及设计系统的能力 .本文在分析现实世界设计活动的基础上 ,提出了一种支持设计环境中学习的软件设计agent的框架结构 ,并介绍了多 agent系统中设计知识的表示和存储 ,及实现在设计者和多个设计 agent间共享学习的设计环境及方式     

Deep learning based robot cognitive architecture for collaborative assembly tasks

As the manufacturing industry becomes more agile, the use of collaborative robots capable of safely working with humans is becoming more prevalent, while adaptable and natural interaction is a goal yet to be achieved. This work presents a cognitive architecture composed of perception and reasoning modules that allows a robot to adapt its actions while collaborating with humans in an assembly task. Human action recognition perception is performed using convolutional neural network models with inertial measurement unit and skeleton tracking data. The action predictions are used for task status reasoning which predicts the time left for each action in a task allowing a robot to plan future actions. The task status reasoning uses a recurrent neural network method which is developed for transferability to new actions and tasks. Updateable input parameters allowing the system to optimise for each user and task with each trial performed are also investigated. Finally, the complete system is demonstrated with the collaborative assembly of a small chair and wooden box, along with a solo robot task of stacking objects performed when it would otherwise be idle. The human actions recognised are using a screw driver, Allen key, hammer and hand screwing, with online accuracies between 83–92%. User trials demonstrate the robot deciding when to start collaborative actions in order to synchronise with the user, as well as deciding when it has time to complete an action on its solo task before a collaborative action is required.     

The AGILO Robot Soccer Team—Experience-Based Learning and Probabilistic Reasoning in Autonomous Robot Control

This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot's belief state with respect to the current game situation using a simple off-the-shelf camera system. The estimated game state comprises the positions and dynamic states of the robot itself and its teammates as well as the positions of the ball and the opponent players. Employing sophisticated probabilistic reasoning techniques and exploiting the cooperation between team mates, the robot can estimate complex game states reliably and accurately despite incomplete and inaccurate sensor information. The action selection module selects actions according to specified selection criteria as well as learned experiences. Automatic learning techniques made it possible to develop fast and skillful routines for approaching the ball, assigning roles, and performing coordinated plays. The paper discusses the computational techniques based on experimental data from the 2001 robot soccer world championship.     

事件本体中的动作推理研究

事件是随时间变化而变化的具体事实,事件是由动作、时间及其它要素组成,动作是事件定义中的主要构成部分.在面向事件的知识库系统中,关于动作的推理研究一直是重要的研究课题之一.现有的动作推理形式化系统旨在描述和推理现实世界中状态的变化,忽略了时间要素对推理过程的影响.针对这种不足,本文在描述逻辑的基础上扩充了一个Action-TBox和一个Action-ABox,并将事件本体中的动作要素和时间要素相结合,形式化定义了动作的一个三元组表示方式以及多种时间构造算子,用以刻画组合动作的发生过程,在此基础上研究了事件本体中关于动作的几种推理服务.     

Learning Concepts from Sensor Data of a Mobile Robot

Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm GRDT has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars.     

From event calculus to the scheduling problem. Semantics of action and temporal reasoning in aircraft maintenance

In this work we are interested in the logical and semantical aspects of reasoning about actions in a scheduling process. We present an adaptation of the event calculus of Kowalski and Sergot to the problem of determining the temporal structure of the operations that must be performed during the realization of some complex objectives. Our application domain is aircraft maintenance. We try to reason about the actions which are performed during an overhaul in order to help to schedule them. The original model reasons about changes, i.e. events which initiate or terminate propositions. The first step of this work was to improve the initial model by adding a temporal relation between events and propositions because in our field we also have to reason about events which only inform us about some propositions without affecting them. The second step of this work is to build a set of specific rules which temporally interpret the semantics of the usual specifications of the actions to be considered. This interpretation aims to associate each action with two events and some temporal relations which are usable by the general model. Temporal reasoning uses pertinent knowledge about the specific universe (here, the aircraft that we consider and the actions which may be performed on it). We outline a generative methodology to formalize this relevant knowledge efficiently. This cognitive approach brings more informational economy in temporal reasoning because only the relevant information is considered The temporal reasoning model and the methodology have been exemplified and tested on a complex part of an aircraft. In the future, adapted tools based on this approach will be developed, in order to solve several problems of aircraft maintenance scheduling.     

Merging deductive and inductive reasoning for processing textual descriptions of inter-human conflicts

We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive, and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This study demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. Suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.     

BSCRP网:机器人规划的赋时有色网实现方法

为了解决情景演算无法解决框架问题和生成动作序列效率底的问题,提出了一种基于情景演算推理规则的表示机器人规划的赋时有色网实现方法——BSCRP网(representation based on situation calculus for robot plan),并提出了一种基于双向搜索策略的BSCRP网系统的构造方法。实验结果表明了机器人规划的BSCRP网系统不仅能形式化地描述动作、状态以及动作和状态之间的关系,而且能动态地规划出实现目标的动作序列并计算执行动作序列所需时间。     

Iterated belief change in the situation calculus

John McCarthy's situation calculus has left an enduring mark on artificial intelligence research. This simple yet elegant formalism for modelling and reasoning about dynamic systems is still in common use more than forty years since it was first proposed. The ability to reason about action and change has long been considered a necessary component for any intelligent system. The situation calculus and its numerous extensions as well as the many competing proposals that it has inspired deal with this problem to some extent. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular, our approach is based on a well-developed theory of action in the situation calculus extended to deal with belief. Moreover, by augmenting this approach with a notion of plausibility over situations, our account handles nested belief, belief introspection, mistaken belief, and handles belief revision and belief update together with iterated belief change.