Agent Programming in 3APL

An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.To support this new style of programming, we propose the agent programming language 3APL. 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative and logic programming. From imperative programming the language inherits the full range of regular programming constructs, including recursive procedures, and a notion of state-based computation. States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. These features are well-understood and provide a solid basis for a structured agent programming language. Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.Applying the metaphor of intelligent agents means taking a design stance. From this perspective, a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. We illustrate how the metaphor of intelligent agents is supported by the programming language. We also discuss the design of control structures for rule-based agent languages. A control structure provides a solution to the problem of which goals and which rules an agent should select. We provide a concrete and intuitive ordering on the practical reasoning rules on which such a selection mechanism can be based. The ordering is based on the metaphor of intelligent agents. Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.     

An Automated Teamwork Infrastructure for Heterogeneous Software Agents and Humans

Agent integration architectures enable a heterogeneous, distributed set of agents to work together to address problems of greater complexity than those addressed by the individual agents themselves. Unfortunately, integrating software agents and humans to perform real-world tasks in a large-scale system remains difficult, especially due to three main challenges: ensuring robust execution in the face of a dynamic environment, providing abstract task specifications without all the low-level coordination details, and finding appropriate agents for inclusion in the overall system. To address these challenges, our Teamcore project provides the integration architecture with general-purpose teamwork coordination capabilities. We make each agent team-ready by providing it with a proxy capable of general teamwork reasoning. Thus, a key novelty and strength of our framework is that powerful teamwork capabilities are built into its foundations by providing the proxies themselves with a teamwork model.Given this teamwork model, the Teamcore proxies addresses the first agent integration challenge, robust execution, by automatically generating the required coordination actions for the agents they represent. We can also exploit the proxies' reusable general teamwork knowledge to address the second agent integration challenge. Through team-oriented programming, a developer specifies a hierarchical organization and its goals and plans, abstracting away from coordination details. Finally, KARMA, our Knowledgeable Agent Resources Manager Assistant, can aid the developer in conquering the third agent integration challenge by locating agents that match the specified organization's requirements. Our integration architecture enables teamwork among agents with no coordination capabilities, and it establishes and automates consistent teamwork among agents with some coordination capabilities. Thus, team-oriented programming provides a level of abstraction that can be used on top of previous approaches to agent-oriented programming. We illustrate how the Teamcore architecture successfully addressed the challenges of agent integration in two application domains: simulated rehearsal of a military evacuation mission and facilitation of human collaboration.     

GOAL‐DRIVEN AUTONOMY FOR RESPONDING TO UNEXPECTED EVENTS IN STRATEGY SIMULATIONS

To operate autonomously in complex environments, an agent must monitor its environment and determine how to respond to new situations. To be considered intelligent, an agent should select actions in pursuit of its goals, and adapt accordingly when its goals need revision. However, most agents assume that their goals are given to them; they cannot recognize when their goals should change. Thus, they have difficulty coping with the complex environments of strategy simulations that are continuous, partially observable, dynamic, and open with respect to new objects. To increase intelligent agent autonomy, we are investigating a conceptual model for goal reasoning called Goal‐Driven Autonomy (GDA), which allows agents to generate and reason about their goals in response to environment changes. Our hypothesis is that GDA enables an agent to respond more effectively to unexpected events in complex environments. We instantiate the GDA model in ARTUE (A utonomous R esponse t o U nexpected E vents), a domain‐independent autonomous agent. We evaluate ARTUE on scenarios from two complex strategy simulations, and report on its comparative benefits and limitations. By employing goal reasoning, ARTUE outperforms an off‐line planner and a discrepancy‐based replanner on scenarios requiring reasoning about unobserved objects and facts and on scenarios presenting opportunities outside the scope of its current mission.     

A basic agent

A basic agent has been constructed which integrates limited natural language understanding and generation, temporal planning and reasoning, plan execution, simulated symbolic perception, episodic memory, and some general world knowledge. The agent is cast as a robot submarine operating in a two-dimensional simulated "Seaworld" about which it has only partial knowledge. It can communicate with people in a vocabulary of about 800 common English words using a medium coverage grammar. The agent maintains an episodic memory of events in its life and has a limited ability to reflect on those events. A person can make statements to the agent, ask it questions, and give it commands. In response to commands, a temporal task planner is invoked to synthesize a plan, which is then executed at an appropriate future time. A large variety of temporal references in natural language are interpreted with respect to agent time. The agent can form and retain compound future plans, and replan in response to new information or new commands. Natural language verbs are represented in a state transition semantics for compatibility with the planner. The agent is able to give terse answers to questions about its past experiences, present activities and perceptions, future intentions, and general knowledge. No other artificial intelligence artifact with this range of capabilities has previously been constructed.     

基于Agent的智能CBR引导系统的结构设计

描述了一个应用软件Agent技术的智能CBR引导系统的结构设计。系统机架在引导界面Agent里融合了CBR思想。介绍了分布式的引导Agent的交互、引导系统的多媒体界面的设计思想，以及系统在专家系统开发工具上的应用。     

A Computational Framework for Practical Social Reasoning

This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called the expectation strategy behavior (ESB) framework, is based on (i) using sets of update rules for social beliefs tied to observations (so‐called expectations), (ii) bounding the amount of reasoning to be performed over these rules by defining a reasoning strategy, and (iii) influencing the agent's decision‐making logic by means of behaviors conditioned on the truth status of current and future social beliefs. We introduce the foundations of ESB conceptually and present a formal framework and an actual implementation of a reasoning engine, which is specifically combined with a general (belief–desire–intention‐based) practical reasoning programming system. We illustrate the generality of ESB through select case studies, which show that it is able to represent and implement different typical styles of social reasoning. The broad coverage of existing social reasoning methods, the modularity that derives from its declarative nature, and its focus on practical implementation make ESB a useful tool for building advanced socially reasoning agents.     

自适应BDI主体研究

该文首先通过对两种智能主体的介绍，分析当前智能主体研究处理非预期情况的两种主要途径，即反应式和慎思式，指出各自存在的问题。进而提出自适应BDI主体的基本框架，在此基础上，论述建立慎思式智能主体适应机制的必要性和可能性。通过引入原子以及公式的相关性，给出意图、目标的一个基于逻辑概念的形式刻画，进而给出“愿望保持性”和“承诺可传递性”的一个近似规范。基于这一规范，可以实现非预期情况下的愿望修正，并支持承诺调整，给出其若干关键问题的形式化结论。     

Agent组织中的政策导向型协作模型

传统Agent协作模型强调Agent的高度自主性,其自发协作过程完全出于内部的"自私性"动机,难以在模型中表达宏观层面上的引导及系统外部的约束,在应用于开放复杂软件系统时,将面临可信度不能满足要求、计算复杂度高及没有有效的冲突消解机制这3方面的问题,这阻碍了Agent技术在现实软件系统建模中的应用.采用组织与政策隐喻,提出Agent组织中的政策导向型协作模型,通过组织与政策给予Agent以宏观上的引导与外部的控制,以增强系统的可信度;采用扩展的可废止逻辑框架,对这一协作过程建立一个具有线性计算复杂度的形式化理论;逻辑体系中内置的优先级方式为系统提供了有效的冲突消解机制.并证明了模型所具有的一致性及其他特性,最后通过一个实例对系统作出验证性说明.     

Architectural Methodology Based on Intentional Configuration of Behaviors

Intelligence has been an object of study for a long time. Different architectures try to capture and reproduce these aspects into artificial systems (or agents), but there is still no agreement on how to integrate them into a general framework. With this objective in mind, we propose an architectural methodology based on the idea of intentional configuration of behaviors. Behavior‐producing modules are used as basic control components that are selected and modified dynamically according to the intentions of the agent. These intentions are influenced by the situation perceived, knowledge about the world, and internal variables that monitor the state of the agent. The architectural methodology preserves the emergence of functionality associated with the behavior‐based paradigm in the more abstract levels involved in configuring the behaviors. Validation of this architecture is done using a simulated world for mobile robots, in which the agent must deal with various goals such as managing its energy and its well‐being, finding targets, and acquiring knowledge about its environment. Fuzzy logic, a topologic map learning algorithm, and activation variables with a propagation mechanism are used to implement the architecture for this agent.     

基于关系的两维意向结构

从建构agent角度出发,提出了一个基于关系结构的包括agent意向、信念以及目标等认知状态的框架.在此框架中,实现目标的意向形成了两维序结构,其中一维表示意向间的时序关系,另一维表示意向间的相干关系,在此基础上,研究了信念、意向和目标的相互关系.因为摒弃了传统的用模态算子来刻画agent的意向的方法,所以在构建agent时,可以直接采用意向库以及意向间的时序、相干关系来表示agent的意向,从而缩小了agent理论模型与实际agent结构之间的差异,为agent结构的建立提供了必要的理论基础.     

多Agent系统的技术研究

多Agent系统是由多个智能Agent组成的有机系统,这使得它具有比单个Agent更强大的处理能力。它表出自组织性、鲁棒性、分布性以及很强的复杂行为。文中论述了Agent和多Agent系统的有关理论、方法和技术。主要包括智能Agent的特性、结构和推理;介绍多Agent系统的体系结构分类和常见的几种通信机制;以及面向Agent的程序设计的现状和发展。     

Agent主动目标的形式化模型

在agent结构中,主动目标是一个功能上自含且有自己独立控制流的实体.给出了主动目标相关的语法定义以及主动目标运行的操作语义,而且主动目标驱动下的BDI agent结构也被形式化地定义出来.区别于以往的一些BDI agent结构,目标不是隐含地表示而是作为实体显式地表示在agent结构中,使agent结构很自然地支持并行的目标,这被认为是agent理性行为的一个重要方面.此外,对目标的显式定义也为agent在动态的环境中对承诺的重新考虑带来了方便.     

Towards True Dynamic Workflow for Emergency Response

Emergency management is a process by which all individuals, groups, and communities manage hazards in an effort to avoid or ameliorate the impact of disasters resulting from the hazards. Emergency response workflow is dynamic because there are lots of uncertainties with the course of hazard development and rescue effort. Existing dynamic workflow modeling technologies are not su±cient to describe the complex emergency response processes which are context aware and data-driven. In this paper, we propose an intelligent agent based approach to supporting the emergency response process management. The approach integrates BDI (Belief-Desire-Intention) agents with WIFA workflow model, which was developed in our previous work, to a powerful tool for truly dynamic workflow modeling and enactment. A BDI agent is an intelligent agent. Beliefs represent the informational state of the agent - in other words its beliefs about the world. Desires (or goals) represent the motivational state of the agent. They represent objectives or situations that the agent would like to accomplish or bring about. Intentions represent the deliberative state of the agent: what the agent has chosen to do. Intentions are desires to which the agent has to some extent committed. Workflows represent sequences of actions that an agent can perform to achieve one or more of its intentions. Based on this approach, we developed an emergency response training tool which is customizable for individual organization use and scalable to incident response settings from rural to urban domestically and foreign outposts for military applications, and can operate at a holistic exercise level.     

Planning Intelligent Responses in a Natural Language System

Intelligent help systems cannot merely respond passively to the user'scommands and queries. They need to be able to volunteer information,correct user misconceptions, and reject unethical requests when appropriate.In order to do these things, a system must be designed as an intelligentagent. That is, a system needs to have its own goals and then plan forthese goals. A system which did not have its own goals would never refuseto help users perform unethical actions.Such an intelligent agent has been implemented in the UCEgo component of UC(Wilensky et al. 1984; Wilensky et al. 1988) (UNIX Consultant), a natural languagesystem that helps the user solve problems in using the UNIX operatingsystem. UCEgo provides UC with its own goals and plans. By adoptingdifferent goals in different situations, UCEgo creates and executesdifferent plans, enabling it to interact appropriately with the user.UCEgo adopts goals when it notices that the user either lacks necessaryknowledge, or has incorrect beliefs. In these cases, UCEgo plans tovolunteer information or correct the user's misconception as appropriate.These plans are pre-stored skeletal plans that are indexed under the types ofsituations in which they are typically useful. Plan suggestion situationsinclude the goal which the plan is used to achieve, the preconditions of theplan, and appropriateness conditions for the plan. Indexing plans bysituations improves efficiency and allows UC to respond appropriately to theuser in real time.Detecting situations in which a plan should be suggested or a goal adoptedis implemented using if-detected daemons. These daemons provide asingle mechanism which can be used both for detecting goals and suggestingplans. Different methodologies for the efficient implementation ofif-detected daemons are discussed.     

Design and implementation of a multiagent stock trading system

Stock trading is one of the key items in an economy and estimating its behavior and taking the best decision in it are among the most challenging issues. Solutions based on intelligent agent systems are proposed to cope with those challenges. Agents in a multiagent system (MAS) can share a common goal or they can pursue their own interests. That nature of MASs exactly fits the requirements of a free market economy. Although existing studies include noteworthy proposals on agent‐based market simulation and researchers discuss theoretical design issues of agent‐based stock exchange systems, unfortunately only a very few of the studies consider exact development and implementation of multiagent stock trading systems within the software engineering perspective and guides to the software engineers for constructing such software systems starting from scratch. To fill this gap, in this paper, we discuss the development of a multiagent‐based stock trading system by taking into consideration software design according to a well‐defined agent oriented software engineering methodology and implementation with a widely‐used MAS software development framework. Each participant in the system is first designed as belief–desire–intention agents with their facts, goals, and plans, and then belief–desire–intention reasoning and behavioral structure of the designed agents are implemented. Lessons learned during design and development within the software engineering perspective and evaluation of the implemented multiagent stock exchange system are also reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Computationally Effective Reasoning About Goal Interactions

It is important that intelligent agents are able to pursue multiple goals in parallel, in a rational manner. In this work we have described the careful empirical evaluation of the value of data structures and algorithms developed for reasoning about both positive and negative goal interactions. These mechanisms are incorporated into a commercial agent platform and then evaluated in comparison to the platform without these additions. We describe the data structures and algorithms developed, and the X-JACK system, which incorporates these into JACK, a state of the art agent development toolkit. There are three basic kinds of reasoning that are developed: reasoning about resource conflicts, reasoning to avoid negative interactions that can happen when steps of parallel goals are arbitrarily interleaved, and reasoning to take advantage of situations where a single step can help to achieve multiple goals. X-JACK is experimentally compared to JACK, under a range of situations designed to stress test the reasoning algorithms, as well as situations designed to be more similar to real applications. We found that the cost of the additional reasoning is small, even with large numbers of goal interactions to reason about. The benefit however is noticeable, and is statistically significant, even when the amount of goal interactions is relatively small.     

A logic-based model of intention formation and action for multi-agent subcontracting

We present a formalism for representing the formation of intentions by agents engaged in cooperative activity. We use a syntactic approach presenting a formal logical calculus that can be regarded as a meta-logic that describes the reasoning and activities of the agents. Our central focus is on the evolving intentions of agents over time, and the conditions under which an agent can adopt and maintain an intention. In particular, the reasoning time and the time taken to subcontract are modeled explicitly in the logic. We axiomatize the concept of agent interactions in the meta-language, show that the meta-theory is consistent and describe the unique intended model of the meta-theory. In this context we deal both with subcontracting between agents and the presence of multiple recipes, that is, multiple ways of accomplishing tasks. We show that under various initial conditions and known facts about agent beliefs and abilities, the meta-theory representation yields good results.     

The Self as an Embedded Agent

In this paper we consider the concept of a self-aware agent. In cognitive science agents are seen as embodied and interactively situated in worlds. We analyse the meanings attached to these terms in cognitive science and robotics, proposing a set of conditions for situatedness and embodiment, and examine the claim that internal representational schemas are largely unnecessary for intelligent behaviour in animats. We maintain that current situated and embodied animats cannot be ascribed even minimal self-awareness, and offer a six point definition of embeddedness, constituting minimal conditions for the evolution of a sense of self. This leads to further analysis of the nature of embodiment and situatedness, and a consideration of whether virtual animats in virtual worlds could count as situated and embodied. We propose that self-aware agents must possess complex structures of self-directed goals; multi-modal sensory systems and a rich repertoire of interactions with their worlds. Finally, we argue that embedded agents will possess or evolve local co-ordinate systems, or points of view, relative to their current positions in space and time, and have a capacity to develop an egocentric space. None of these capabilities are possible without powerful internal representational capacities.     

Argumentation and the Dynamics of Warranted Beliefs in Changing Environments

One of the most difficult problems in Multi-Agent Systems (MAS) involves representing the knowledge and beliefs of an agent which performs its tasks in a dynamic environment. New perceptions modify this agent’s current knowledge about the world, and consequently its beliefs about it also change. Such a revision and update process should be performed efficiently by the agent, particularly in the context of real-time constraints. In the last decade argumentation has evolved as a successful approach to formalize defeasible, commonsense reasoning, gaining wide acceptance in the MAS community by providing tools for designing and implementing features, which characterize reasoning capabilities in rational agents. In this paper we present a new argument-based formalism specifically designed for representing knowledge and beliefs of agents in dynamic environments, called Observation-based Defeasible Logic Programming (ODeLP). A simple but effective perception mechanism allows an ODeLP-based agent to model new incoming perceptions, and modify the agent’s knowledge about the world accordingly. In addition, in order to improve the reactive capabilities of ODeLP-based agents, the process of computing beliefs in a changing environment is made computationally attractive by integrating a “dialectical database” with the agent’s program, providing pre-compiled information about previous inferences. We present algorithms for managing dialectical databases as well as examples of their use in the context of real-world problems.