Mean‐field games for multiagent systems with multiplicative noises

This paper studies mean‐field games for multiagent systems with control‐dependent multiplicative noises. For the general systems with nonuniform agents, we obtain a set of decentralized strategies by solving an auxiliary limiting optimal control problem subject to consistent mean‐field approximations. The set of decentralized strategies is further shown to be an ε‐Nash equilibrium. For the integrator multiagent systems, we design a set of ε‐Nash strategies by exploiting the convexity property of the limiting problem. It is shown that under the mild conditions, all the agents achieve mean‐square consensus.     

Internal model approach to cooperative robust output regulation for linear uncertain time‐delay multiagent systems

In this paper, we study the cooperative robust output regulation problem for linear uncertain multiagent systems with both communication delay and input delay by the distributed internal model approach. The problem includes the leader‐following consensus problem of linear multiagent systems with time delay as a special case. We first generalize the internal model design method to systems with both communication delay and input delay. Then, under a set of standard assumptions, we have obtained the solution to the problem via both the state feedback control law and the output feedback control law. In contrast to the existing results, our results apply to general linear uncertain multiagent systems, accommodate a large class of leader signals, and achieve asymptotic tracking and disturbance rejection at the same time.     

A LATTICE‐BASED APPROACH TO THE PROBLEM OF RECRUITMENT IN MULTIAGENT SYSTEMS

Multiagent systems constitute an independent topic at the intersection between distributed computing and artificial intelligence. As the algorithmic techniques and the applications for multiagent systems have been continuously developing over the last two decades reaching significantly mature stages, many methodological problems have been addressed. In this paper, we aim to contribute to this methodological assessment of multiagent systems by considering the problem of choosing, or recruiting, a subset of agents from a set of available agents to satisfy a given request. This problem, which we call problem of recruitment, is encountered, for example, in matchmaking and in task allocation. We present and study a novel formal approach to the problem of recruitment, based on the algebraic formalism of lattices. The resulting formal framework can support the development of algorithms for automatic recruitment.     

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton–Jacobi–Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.     

Intelligence in the internet age: The emergence and evolution of Open Source Intelligence (OSINT)

This paper introduces the concept of Open Source Intelligence (OSINT) as an important component for understanding human problem solving in the 21st century. OSINT is in many ways the result of changing human–information relationships resulting from the emergence and growing dominance of the Internet and the World Wide Web in everyday life. This paper suggests that the Internet/Web changes the dynamic relationship between what Cattell and Horn have identified as the two general factors of human intelligence: crystallized intelligence and fluid intelligence. The Internet/Web open up new possibilities for accessing information and transcending over-determined cultural intelligence in problem solving. This offers fluid intelligence, which often trails off in adulthood, a new vitality across the lifespan. But the diminishment of crystallized intelligence, and especially cultural intelligence, also presents a number of important problems in maintenance of cohesive, social cooperatives. The development of OSINT (using tools and ethos created by the Open Source movement of the last few decades) offers both a framework for reaching beyond the boundaries of traditional cultural intelligence and ways to create cooperative, open, problem solving communities. The Internet/Web will continue to create confusion and fear as we move deeper into this new age, but also presents extraordinary possibilities for augmenting human intellect if we can understand it and learn to harness its potential.     

H2 and H∞ almost output synchronization of heterogeneous continuous‐time multi‐agent systems with passive agents and partial‐state coupling via static protocol

This paper studies H₂ and H_∞ almost output synchronization problems for heterogeneous continuous‐time multiagent systems with passive agents and strongly connected communication graph. For non‐introspective passive agents, a linear static protocol can be designed to achieve almost output synchronization with arbitrarily small H₂ norm. Moreover, we show that the H_∞ almost output synchronization problem via static protocol is not solvable for this class of systems.     

A logical foundation for the case‐based reasoning cycle

Case‐based reasoning (CBR) has drawn considerable attention in artificial intelligence (AI) fields with many successful applications in systems such as e‐commerce and multiagent systems. For the moment, research and development of CBR basically follows the traditional process model of CBR, i.e., the R⁴ model and problem space model introduced in 1994 and 1996, respectively. However, there has been no logical analysis for this popular CBR model. This article will fill this gap by providing a unified logical foundation for the CBR cycle. The proposed approach is based on an integration of traditional mathematical logic, fuzzy logic, and similarity‐based reasoning. At the same time, we examine the CBR cycle from the knowledge‐based (KB) viewpoint. The proposed logical approach can facilitate research and development of CBR. © 2003 Wiley Periodicals, Inc.     

ACHIEVING FLEXIBLE AUTONOMY IN MULTIAGENT SYSTEMS USING CONSTRAINTS

Organizations influence many aspects of our lives. They exist for one reason: they can accomplish things that individuals cannot. While recent work in high-autonomy systems has shown that autonomy is a critical issue in artificial intelligence (AI) systems, these systems must also be able to cooperate with and rely on one another to deal with complex problems. The autonomy of such systems must be flexible, in order that agents may solve problems on their own as well as in groups. We have developed a model of distributed problem solving in which coordination of problem-solving agents is viewed as a multiagent constraint-satisfaction planning problem. This paper describes the experimental testbed that we are currently developing to facilitate the investigation of various constraint-based strategies for addressing the coordination issues inherent in cooperative distributed problem-solving domains.     

Soccer server: A tool for research on multiagent systems

This article describes Soccer Server, a simulator of the game of soccer designed as a benchmark for evaluating multiagent systems and cooperative algorithms. In real life, successful soccer teams require many qualities, such as basic ball control skills, the ability to carry out strategies, and teamwork. We believe that simulating such behaviors is a significant challenge for computer science, artificial intelligence, and robotics technologies. It is to promote the development of such technologies, and to help define a new standard problem for research, that we have developed Soccer Server. We demonstrate the potential of Soccer Server by reporting an experiment that uses the system to compare the performance of a neural network architecture and a decision tree algorithm at learning the selection of soccer play plans. Other researchers using Soccer Server to investigate the nature of cooperative behavior in a multiagent environment will have the chance to assess their progress at RoboCup-97, an international competition of robotic soccer to be held in conjunction with IJCAI-97. Soccer Server has been chosen as the official server for this contest.     

Algorithms for the On-Line Travelling Salesman 1

In this paper the problem of efficiently serving a sequence of requests presented in an on-line fashion located at points of a metric space is considered. We call this problem the On-Line Travelling Salesman Problem (OLTSP). It has a variety of relevant applications in logistics and robotics. We consider two versions of the problem. In the first one the server is not required to return to the departure point after all presented requests have been served. For this problem we derive a lower bound on the competitive ratio of 2 on the real line. Besides, a 2.5 -competitive algorithm for a wide class of metric spaces, and a 7/3 -competitive algorithm for the real line are provided. For the other version of the problem, in which returning to the departure point is required, we present an optimal 2 -competitive algorithm for the above-mentioned general class of metric spaces. If in this case the metric space is the real line we present a 1.75 -competitive algorithm that compares with a \approx 1.64 lower bound. Received November 12, 1997; revised June 8, 1998.     

Framework of agent-based intelligence system with two-stage decision-making process for distributed dynamic scheduling

The advent of multiagent systems, a branch of distributed artificial intelligence, introduced a new approach to problem solving through agents interacting in the problem solving process. In this paper, a collaborative framework of a distributed agent-based intelligence system is addressed to control and resolve dynamic scheduling problem of distributed projects for practical purposes. If any delay event occurs, the self-interested activity agent, the major agent for the problem solving of dynamic scheduling in the framework, can automatically cooperate with other agents in real time to solve the problem through a two-stage decision-making process: the fuzzy decision-making process and the compensatory negotiation process. The first stage determines which behavior strategy will be taken by agents while delay event occurs, and prepares to next negotiation process; then the compensatory negotiations among agents are opened related with determination of compensations for respective decisions and strategies, to solve dynamic scheduling problem in the second stage. A prototype system is also developed and simulated with a case to validate the problem solving of distributed dynamic scheduling in the framework.     

The design of puzzle selection strategies for GWAP systems

The Games With A Purpose (GWAP) genre is a type of Human Computation that outsources certain steps of the computational process to humans. By taking advantage of people's desire to be entertained, GWAP attracts people to play voluntarily, and also produce useful metadata as a by‐product. The games have shown promise in solving a variety of problems, which computer computation has been unable to resolve completely thus far. In this paper, we propose a metric, called system gain, for evaluating the performance of GWAP systems, and also use analysis to study the properties of GWAP systems. We argue that it is important for GWAP systems to implement proper puzzle selection strategies in order to collect human intelligence in a more efficient manner. Therefore, based on our analysis, we implement an Optimal Puzzle Selection Strategy (OPSA) to improve GWAP systems. Using a comprehensive set of simulations, we demonstrate that the proposed OPSA approach can effectively improve the system gain of GWAP systems, as long as the number of puzzles in the system is sufficiently large. Copyright © 2010 John Wiley & Sons, Ltd.     

Cooperative, hybrid agent architecture for real-time traffic signal control

This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time traffic signal control of a complex traffic network. The large-scale traffic signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of signal plans used by the current real-time adaptive traffic control system. The multiagent architecture produces significant improvements in the conditions of the traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.     

H∞ control for a class of cascade switched nonlinear systems

In this paper, we investigate the H_∞ control problem for a class of cascade switched nonlinear systems consisting of two nonlinear parts which are also switched systems using the multiple Lyapunov function method. Firstly, we design the state feedback controller and the switching law, which guarantees that the corresponding closed‐loop system is globally asymptotically stable and has a prescribed H_∞ performance level. This method is suitable for a case where none of the switched subsystems is asymptotically stable. Then, as an application, we study the hybrid H_∞ control problem for a class of nonlinear cascade systems. Finally, an example is given to illustrate the feasibility of our results. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Worst‐case control policies for (terminal) linear–quadratic control problems under disturbances

We consider linear control systems under uncertainties. For such systems we solve the problem of constructing worst‐case feedback control policies that are allowed to be corrected at m fixed intermediate time moments. We propose two types of the approximative control policies. All of them guarantee that for all admissible uncertainties the terminal system state lies in a prescribed neighborhood of a given state x_* at a given final moment, and the value of the cost function does not exceed a given estimate. It is shown that computation of the estimate for each policy is equivalent to solving a corresponding convex mathematical programming (MP) problem with m decision variables. Based on the solution of the MP problem, we derive simple explicit rules (which can be easily implemented on‐line) for constructing the corresponding control policy in the original control problem. Copyright © 2009 John Wiley & Sons, Ltd.     

Relationship between perceived problem‐solving skills and academic performance of novice learners in introductory programming courses

Past research has shown that student problem‐solving skills may be used to determine student final exam performance. This study reports on the relationship between student perceived problem‐solving skills 1 1 Henceforth, for brevity, we drop the word “perceived” in “student perceived problem‐solving skills” and use either “student perceived problem solving” or simply “student PSS.”
and academic performance in introductory programming, in formative and summative programming assessment tasks. We found that the more effective problem solvers achieved better final exam scores. There was no significant difference in formative assessment performances between effective and poor problem solvers. It is also possible to categorize students on the basis of problem‐solving skills, in order to exploit opportunities to improve learning around constructivist learning theory. Finally, our study identified transferability skills and the study may be extended to identify the impact of problem solving transfer skills on student problem solving for programming.     

Brave induction: a logical framework for learning from incomplete information

This paper introduces a novel logical framework for concept-learning called brave induction. Brave induction uses brave inference for induction and is useful for learning from incomplete information. Brave induction is weaker than explanatory induction which is normally used in inductive logic programming, and is stronger than learning from satisfiability, a general setting of concept-learning in clausal logic. We first investigate formal properties of brave induction, then develop an algorithm for computing hypotheses in full clausal theories. Next we extend the framework to induction in nonmonotonic logic programs. We analyze computational complexity of decision problems for induction on propositional theories. Further, we provide examples of problem solving by brave induction in systems biology, requirement engineering, and multiagent negotiation.     

Consensus analysis of hybrid multiagent systems: A game‐theoretic approach

This paper considers a consensus problem for hybrid multiagent systems, which comprise two groups of agents: a group of continuous‐time dynamic agents and a group of discrete‐time dynamic agents. Firstly, a game‐theoretic approach is adopted to model the interactions between the two groups of agents. To achieve consensus for the considered hybrid multiagent systems, the cost functions are designed. Moreover, it is shown that the designed game admits a unique Nash equilibrium. Secondly, sufficient/necessary conditions of solving consensus are established. Thirdly, we find that the convergence speed of the system depends on the game. By the mechanism design of the game, the convergence speed is increased. Finally, simulation examples are given to validate the effectiveness of the theoretical results.     

Fractional‐order–dependent global stability analysis and observer‐based synthesis for a class of nonlinear fractional‐order systems

This paper focuses on proposing novel conditions for stability analysis and stabilization of the class of nonlinear fractional‐order systems. First, by considering the class of nonlinear fractional‐order systems as a feedback interconnection system and applying small‐gain theorem, a condition is proposed for L₂‐norm boundedness of the solutions of these systems. Then, by using the Mittag‐Leffler function properties, we show that satisfaction of the proposed condition proves the global asymptotic stability of the class of nonlinear fractional‐order systems with fractional order lying in (0.5, 1) or (1.5, 2). Unlike the Lyapunov‐based methods for stability analysis of fractional‐order systems, the new condition depends on the fractional order of the system. Moreover, it is related to the H_∞‐norm of the linear part of the system and it can be transformed to linear matrix inequalities (LMIs) using fractional‐order bounded‐real lemma. Furthermore, the proposed stability analysis method is extended to the state‐feedback and observer‐based controller design for the class of nonlinear fractional‐order systems based on solving some LMIs. In the observer‐based stabilization problem, we prove that the separation principle holds using our method and one can find the observer gain and pseudostate‐feedback gain in two separate steps. Finally, three numerical examples are provided to demonstrate the advantage of the novel proposed conditions with the previous results.     

Task Muddiness, Intelligence Metrics, and the Necessity of Autonomous Mental Development

This paper introduces a concept called task muddiness as a metric for higher intelligence. Task muddiness is meant to be inclusive and expendable in nature. The intelligence required to execute a task is measured by the composite muddiness of the task described by multiple muddiness factors. The composite muddiness explains why many challenging tasks are muddy and why autonomous mental development is necessary for muddy tasks. It facilitates better understanding of intelligence, what the human adult mind can do, and how to build a machine to acquire higher intelligence. The task-muddiness indicates a major reason why a higher biological mind is autonomously developed from autonomous, simple-to-complex experience. The paper also discusses some key concepts that are necessary for understanding the mind and intelligence, such as intelligence metrics, the mode a task is conveyed to the task executor, a human and a machine being a joint task performer in the traditional artificial intelligence (AI), a developmental agent (human or machine) being a sole task performer, and the need for autonomy in task-nonexplicit learning.