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.     

Adaptive goal selection for agents in dynamic environments

In psychology, goal-setting theory, which has been studied by psychologists for over 35 years, reveals that goals play significant roles in incentive, action and performance for human beings. Based on this theory, a goal net model has been proposed to design intelligent agents that can be viewed as a soft copy of human being somehow. The goal net model has been successfully applied in many agents, specially, non-player-character agents in computer games. Such an agent selects the optimal solution in all possible solutions found by using a recursive algorithm. However, if a goal net is very complex, the time of selection could be too long for the agent to respond quickly when the agent needs to re-select a new solution against the world’s change. Moreover, in some dynamic environments, it is impossible to know the exact outcome of choosing a solution in advance, and so the possible solutions cannot be evaluated precisely. Thus, to address the problem, this paper applies learning algorithm into goal selection in dynamic environments. More specifically, we first develop a reorganization algorithm that can convert a goal net to its equivalent counterpart that a Q-learning algorithm can operate on; then, we define the key component of Q-learning, reward function, according to the feature of goal nets; and finally lots of experiments are conducted to show that, in dynamic environments, the agent with the learning algorithm significantly outperforms the one with the recursive searching algorithm. Therefore, our work suggests an agent model that can effectively be applied in dynamic time-sensitive domain, like computer games and the P2P systems of online movie watching.     

SOLVING ABSTRACT COOPERATIVE PATH‐FINDING IN DENSELY POPULATED ENVIRONMENTS

The problem of cooperative path‐finding is addressed in this work. A set of agents moving in a certain environment is given. Each agent needs to reach a given goal location. The task is to find spatial temporal paths for agents such that they eventually reach their goals by following these paths without colliding with each other. An abstraction where the environment is modeled as an undirected graph is adopted—vertices represent locations and edges represent passable regions. Agents are modeled as elements placed in the vertices while at most one agent can be located in a vertex at a time. At least one vertex remains unoccupied to allow agents to move. An agent can move into unoccupied neighboring vertex or into a vertex being currently vacated if a certain additional condition is satisfied. Two novel scalable algorithms for solving cooperative path‐finding in bi‐connected graphs are presented. Both algorithms target environments that are densely populated by agents. A theoretical and experimental evaluation shows that the suggested algorithms represent a viable alternative to search based techniques as well as to techniques employing permutation groups on the studied class of the problem.     

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.     

A symbolic solution to intelligent real-time control

Autonomous systems must operate in dynamic, unpredictable environments in real time. The task of flying a plane is an example of an environment in which the agent must respond quickly to unexpected events while pursuing goals at different levels of complexity and granularity. We present a system, Air-Soar, that achieves intelligent control through fully symbolic reasoning in a hierarchy of simultaneously active problem spaces. Achievement goals, changing to a new state, and homeostatic goals, continuously maintaining a constraint, are smoothly integrated within the system. The hierarchical approach and support for multiple, simultaneous goals give rise to multi-level reactive behavior, in which Air-Soar responds to unexpected events at the same granularity where they are first sensed.     

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。     

Aborting,suspending, and resuming goals and plans in BDI agents

Intelligent agents designed to work in complex, dynamic environments such as e-commerce must respond robustly and flexibly to environmental and circumstantial changes, including the actions of other agents. An agent must have the capability to deliberate about appropriate courses of action, which may include reprioritising tasks—whether goals or associated plans—aborting or suspending tasks, or scheduling tasks in a particular order. In this article we study mechanisms to enable principled suspend, resuming, and aborting of goals and plans within a Belief-Desire-Intention (BDI) agent architecture. We give a formal and combined operational semantics for these actions in an abstract agent language (CAN), thus providing a general mechanism that can be incorporated into several BDI-based agent platforms. The abilities enabled by our semantics provides an agent designer greater flexibility to direct agent operation, offering a generic means to manage the status of goals. We demonstrate the reasoning abilities enabled on a document workflow scenario.     

Multi-goal economic search using dynamic search structures

This paper investigates cooperative search strategies for agents engaged in costly search in a complex environment. Searching cooperatively, several search goals can be satisfied within a single search effort. Given the searchers’ preferences, the goal is to conduct a search in a way that the expected overall utility out of the set of opportunities found (e.g., products when operating in a market) minus the costs associated with finding that set is maximized. This search scheme, given in the context of a group search, applies also to scenarios where a single agent has to search for a set of items for satisfying several different goals. The uniqueness of the proposed mechanism is in the ability to partition the group of agents/goals into sub-groups where the search continues for each group autonomously. As we show throughout the paper, this strategy is favorable as it weakly dominates (i.e., can improve but never worsen) cooperative and autonomous search techniques. The paper presents a comprehensive analysis of the new search method and highlights the specific characteristics of the optimal search strategy. Furthermore, we introduce innovative algorithms for extracting the optimal search strategy in a range of common environments, that eliminates the computational overhead associated with the use of the partitioning technique.     

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.     

To Plan or to Simply React? An Experimental Study of Action Planning in a Game Environment

Many contemporary computer games, notably action and role‐playing games, represent an interesting class of navigation‐intensive dynamic real‐time simulations inhabited by autonomous intelligent virtual agents (IVAs). Although higher level reasoning of IVAs in these domains seems suited for action planning, planning is not widely adopted in existing games and similar applications. Moreover, statistically rigorous study measuring performance of planners in decision making in a game‐like domain is missing. Here, five classical planners were connected to the virtual environment of Unreal Development Kit along with a planner for delete‐free domains (only positive preconditions and positive effects). Performance of IVAs employing those planners and IVAs with reactive architecture was measured on a class of game‐inspired test environments of various sizes and under different levels of external interference. The analysis has shown that planning agents outperform reactive agents if (i) the size of the problem is small or if (b) the environment changes are either hostile to the agent or infrequent. In delete‐free domains, specialized approaches are inferior to classical planners because the lower expressivity of delete‐free domains results in lower plan quality. These results can help to determine when planning is advantageous in games and for IVAs control in other dynamic real‐time environments.     

The effects of viscosity in choice and refusal IPD environments

An objective of multi-agent systems is to build robust intelligent systems capable of existing in complex environments. These systems are often characterised as being uncertain and open to change which make such systems far more difficult to design and understand. Some of this uncertainty and change occurs in open agent environments where agents can freely enter and exit the system. In this paper we will examine this form of population change in a game theoretic setting. These simulations involve studying population change through a number of alternative viscosity models. The simulations will examine two possible trust models. All our simulations will use a simple choice and refusal game environment within which agents may freely choose with which of their peers to interact.     

Character navigation in dynamic environments based on optical flow

Steering and navigation are important components of character animation systems to enable them to autonomously move in their environment. In this work, we propose a synthetic vision model that uses visual features to steer agents through dynamic environments. Our agents perceive optical flow resulting from their relative motion with the objects of the environment. The optical flow is then segmented and processed to extract visual features such as the focus of expansion and time‐to‐collision. Then, we establish the relations between these visual features and the agent motion, and use them to design a set of control functions which allow characters to perform object‐dependent tasks, such as following, avoiding and reaching. Control functions are then combined to let characters perform more complex navigation tasks in dynamic environments, such as reaching a goal while avoiding multiple obstacles. Agent's motion is achieved by local minimization of these functions. We demonstrate the efficiency of our approach through a number of scenarios. Our work sets the basis for building a character animation system which imitates human sensorimotor actions. It opens new perspectives to achieve realistic simulation of human characters taking into account perceptual factors, such as the lighting conditions of the environment.     

Scaling up agent coordination strategies

Deploying intelligent agents to do peoples' bidding in environments ranging from Internet marketplaces to Mars has received much attention. Exactly what an agent is and in what sense a computational agent can behave intelligently remain the subject of considerable debate. However, most would agree that coordination, an agent's fundamental capability to decide on its own actions in the context of the activities of other agents around it, is a central concern of intelligent agency. The value of an intelligent agent coordination strategy lies in how well it scales along various dimensions of stress. Understanding the agent population, its task environment, and expectations about its collective behavior are central to mapping the space of potential approaches. The paper discusses agent coordination and dimensions of coordination stress     

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 user modeling approach for reasoning about interaction sensitive to bother and its application to hospital decision scenarios

In this paper, we present a framework for interacting with users that is sensitive to the cost of bother and then focus on its application to decision making in hospital emergency room scenarios. We begin with a model designed for reasoning about interaction in a single-agent single-user setting and then expand to the environment of multiagent systems. In this setting, agents consider both whether to ask other agents to perform decision making and at the same time whether to ask questions of these agents. With this fundamental research as a backdrop, we project the framework into the application of reasoning about which medical experts to interact with, sensitive to possible bother, during hospital decision scenarios, in order to deliver the best care for the patients that arrive. Due to the real-time nature of the application and the knowledge-intensive nature of the decisions, we propose new parameters to include in the reasoning about interaction and sketch their usefulness through a series of examples. We then include a set of experimental results confirming the value of our proposed approach for reasoning about interaction in hospital settings, through simulations of patient care in those environments. We conclude by pointing to future research to continue to extend the model for reasoning about interaction in multiagent environments for the setting of time-critical care in hospital settings.     

Acquisition of intermediate goals for an agent executing multiple tasks

In this paper, an algorithm that acquires the intermediate goals between the initial and goal states is proposed for an agent executing multiple tasks. We demonstrate the algorithm in the problem of rearranging multiple objects. The result shows that the moving distance to transfer the entire objects to their goal configuration is 1/15 of that without using intermediate goals. We experiment using a real robot to confirm that the intermediate goal can be adapted to a real environment. Our experimental results showed that an agent could adapt the intermediate goals, which were acquired in the simulation, to the experimental environment.     

Sensors + agents + networks = aware agents

Software agents are being deployed in increasing numbers to help users find and manage information, particularly in open environments such as the Internet. For the most part, they operate independently and are typically designed to be aware only of their users and the environment in which they perform their tasks. Thus, they fail to take advantage of each other's abilities or results. For example, a shopping agent might periodically access several online databases to find the best price for a music CD and then purchase it if the price falls below its user's threshold. Other agents might be tracking prices for the same CD, duplicating each other's work. Similarly, if your agent and an agent for the person in the next cubicle are both browsing the same Web site, two identical data streams arrive on your LAN, using twice the bandwidth actually needed. To be more effective, agents must be aware of each other; therefore, they must acquire models of each other. One way to do this is by exchanging messages. A second form of awareness involves the state of the agent's own environment, including characteristics of the computer on which it is executing and its network connection. A third involves self awareness: knowing its name, age, ontology, goals, areas of expertise and ignorance, and reasoning abilities. Finally, the agent should be aware of its physical environment. The article explains how software agents can develop awareness     

Autonomous construction using scarce resources in unknown environments

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a “real but contrived” experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real world.     

Socially intelligent reasoning for autonomous agents

Socially intelligent agents are autonomous problem solvers that have to achieve their objectives by interacting with other similarly autonomous entities. A major concern, therefore, is with the design of the decision-making mechanism that such agents employ in order to determine which actions to take to achieve their goals. We propose a framework for making socially acceptable decisions, based on social welfare functions, that combines social and individual perspectives in a unified and flexible manner. The framework is realized in an exemplar computational setting and an empirical analysis is made of the relative performance of varying sociable decision-making functions in a range of environments. This analysis is then used to design an agent that adapts its decision-making to reflect the resource constraints that it faces at any given time. A further round of empirical evaluation shows how adding such a meta-level mechanism enhances the performance of the agent by directing reasoning to adopt different strategies in different contexts. Finally, the possibility and efficacy of making the metalevel mechanism adaptive, so that experience of past encounters can be factored into the decision-making, is demonstrated