Smart places: Multi-agent based smart mobile virtual community management system

Now-a-days advances in mobile device technology aim to build complex computational systems providing a maximum level of flexibility, decentralization, simplest form of interactivity, and ease of use. Recently, the launch of the agent-oriented platform JaCaMo and its Android client based platform JaCa-Android have provided an appropriate level of abstraction to build smart mobile client server systems providing these attributes. By using these platforms, we have developed a multi-agent based Smart Mobile Virtual Community Management System (SMVCMS) that makes it possible to provide a decentralized and open management of virtual communities. This paper addresses the design and architecture of our multi-agent server and client application. It elaborates different features of our system; such as how a participant in virtual communities is supported by a Jason agent that encapsulates the logic and the control of the participation in a virtual community (such as publishing posts, notifying members, making recommendations for the user, etc.). It also discusses how the set of CArtAgOartifacts provides the basic functionalities and operations giving access to the functionalities for knowledge exchange in virtual communities, and personal agents onAndroid exploit these artifacts to execute their tasks while achieving their individual and collective goals. We have employed SMVCMS in the context of Smart Cities and found that the system fulfills the desired goals, such as decentralization of community management, personalized automatic management and discovery of communities, autonomy of agents and flexibility so that any agent can create its own community with the maximum level of ease.     

CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents

For a software information agent, operating on behalf of a human owner and belonging to a community of agents, the choice of communicating or not with another agent becomes a decision to take, since communication generally implies a cost. Since these agents often operate as recommender systems, on the basis of dynamic recognition of their human owners’ behaviour and by generally using hybrid machine learning techniques, three main necessities arise in their design, namely (i) providing the agent with an internal representation of both interests and behaviour of its owner, usually called ontology; (ii) detecting inter-ontology properties that can help an agent to choose the most promising agents to be contacted for knowledge-sharing purposes; (iii) semi-automatically constructing the agent ontology, by simply observing the behaviour of the user supported by the agent, leaving to the user only the task of defining concepts and categories of interest. We present a complete MAS architecture, called connectionist learning and inter-ontology similarities (CILIOS), for supporting agent mutual monitoring, trying to cover all the issues above. CILIOS exploits an ontology model able to represent concepts, concept collections, functions and causal implications among events in a multi-agent environment; moreover, it uses a mechanism capable of inducing logical rules representing agent behaviour in the ontology by means of a connectionist ontology representation, based on neural-symbolic networks, i.e., networks whose input and output nodes are associated with logic variables.     

On the sources of complexity in agent design

This article addresses the question of what makes agent design hard. This is a fundamental issue not just for the intelligent agents community, but also for the computer science and software engineering communities in general. The article begins by presenting a simple, informal model of agents and their environments, and then uses this model to frame a discussion on the sources of complexity in agent design. Three key sources of complexity are identified: the agent's environment, the nature of interaction between agent and environment, and the nature of the specification or task for the agent. Following this discussion, the article presents a simple formal model of agents and their environments, which is capable of representing many of these sources of complexity. Using the formal model, a number of stereotypical agent design problems are defined. The article concludes with a discussion of related work and issues.     

Law-abiding and integrity on the Internet: A case for agents

Software agents extend the current, information-based Internet to include autonomous mobile processing. In most countries such processes, i.e., software agents are, however, without an explicit legal status. Many of the legal implications of their actions (e.g., gathering information, negotiating terms, performing transactions) are not well understood. One important characteristic of mobile software agents is that they roam the Internet: they often run on agent platforms of others. There often is no pre-existing relation between the “owner” of a running agent’s process and the owner of the agent platform on which an agent process runs. When conflicts arise, the position of the agent platform administrator is not clear: is he or she allowed to slow down the process or possibly remove it from the system? Can the interests of the user of the agent be protected? This article explores legal and technical perspectives in protecting the integrity and availability of software agents and agent platforms.     

Software agents and agency: A personal information management perspective

This paper considers the concept of agency and the applications of software agents within the field of Personal Information Management (PIM). PIM addresses the complex activities undertaken by individuals when organising their personal information. In the context of Personal Information Management, effective software agents may allow users to obtain information relevant to their tasks, and present it in a form that is directly targeted to the needs of the user.This paper concentrates on the notion of agency and its direct application to PIM tasks. A user-driven approach to the design of agent-based systems is presented. We argue that agent systems will only be successful if both usersand their tasks act as the bases for the design of such systems. An example task domain (searching the World-Wide Web) is introduced and a taxonomy of Web agents for the domain is discussed. Technical issues raised during the preliminary implementation of Web agents are also introduced.     

Connected communities from the standpoint of multi-agent systems

We study the provision of software agents for connected communities, a class of applications aiming to augment the way people interact and socialize in geographically co-located communities such as neighbourhoods. Following a number of experiments that we have carried out in this area, we propose a multi-agent architecture and we study how to instantiate it in order to design a specific connected community system. We further report on the research challenges, the opportunities and risks raised by agent-based connected communities. Abe Mamdani, Ph.D., FIEEE, Feng: He currently holds the Chair of Telecommunications Strategy and Services endowed by Nortel Networks and the Royal Academy of Engineering in the Department of Electrical & electronic engineering. He is well known for his research into fuzzy logic, which started in the early 70s, and for his research into artificial intelligence in telecommunications. He spent two years working at the central British Telecom Research Laboratories within the Network Management Department. His work was concerned with the application of Artificial Intelligence principally to that company’s network management products, but also to the other research and development activities concerned with Artificial Intelligence. His current research is concerned with applications of software agents mostly for the delivery of services. He is the technical advisor to FIPA—Foundation for Intelligent Physical Agents—an International body dealing with the creation of standards in the area of software agents. Professor Mamdani is a Fellow of the Royal Academy of Engineering as well as The Institute of Electrical and Electronic Engineering. Jeremy Pitt, Ph.D.: He is a lecturer in the Intelligent & Interactive Systems group in the Department of Electrical & Electronic Engineering at Imperial College, London. He was awarded a PhD in 1991 from the Department of Computing at Imperial College, where as a Research Fellow he also implemented a number of innovative software tools, prototypes and demonstrators. His research now is focussed on the intersection of HCI, AI, and digital communication services; and he has made a particular contribution to the development of Agent Communication Languages. He has significant experience of project management: currently he is Principal Investigator on the UK EPSRC/Nortel Networks funded project CASBAh, and is Workpackage Leader on the EU-funded MARINER and MAPPA projects. He was Visiting Professor in the Department of Philosophy, University of Ferrara, Italy 1997–1998. Kostas Stathis, Ph.D.: He is currently a research associate in the Intelligent & Interactive Systems group in the Department of Electrical and Electronic Engineering at Imperial College, London. In 1996 he received a Ph.D. from the Logic programming group of the Department of Computing, at Imperial College. From 1988 to 1992 he has worked as a Knowledge-Based Systems Engineer for Numerical Algorithms Group, Ltd, Oxford, UK, developing Knowledge-based Front-ends to software packages. His development work has contributed to the UK Alvey programme GLIMPSE, while his research work has contributed to a number of Esprit projects including: FOCUS, TEMPORA, LiMe and MAPPA. His current research interests include formulating interaction in computational logic, games as a development methodology for interactive systems, globalisation of interactive systems, multi-agent systems for connected communities, management games for training, and mobile agents for e-commerce.     

Designing multi-agent unit tests using systematic test design patterns-(extended version)

Software agents are the basic building blocks in many software systems especially those based on artificial intelligence methods, e.g., reinforcement learning based multi-agent systems (MASs). However, testing software agents is considered a challenging problem. This is due to the special characteristics of agents which include its autonomy, distributed nature, intelligence, and heterogeneous communication protocols. Following the test-driven development (TDD) paradigm, we present a framework that allows MAS developers to write test scenarios that test each agent individually. The framework relies on the concepts of building mock agents and testing common agent interaction design patterns. We analyze the most common agent interaction patterns including pair and mediation patterns in order to provide stereotype implementation for their corresponding test cases. These implementations serve as test building blocks and are provided as a set of ready-for-reuse components in our repository. This way, the developer can concentrate on testing the business logic itself and spare him/her the burden of implementing tests for the underlying agent interaction patterns. Our framework is based on standard components such as the JADE agent platform, the JUnit framework, and the eclipse plug-in architecture. In this paper, we present in details the design and function of the framework. We demonstrate how we can use the proposed framework to define more stereotypes in the code repository and provide a detailed analysis of the code coverage for our designed stereotype test code implementations.     

Building intelligent collaborative interface agents with the ICAGENT development framework

This paper describes the implementation of intelligent collaborative interface agents using the intelligent collaborative agent (ICagent) development framework. In particular, the paper presents the implementation of a collaborative interface agent that acts as a tutor in the context of an educational software application. The agent deliberates socially with users following the SharedPlans model of collaborative activity. Social deliberation requires interface agents to make their desires and intentions clear to the application users, being in constant communication with them, to understand the context of their activity and to reconcile their own and users’ desires in the overall context of action. Reconciliation of users’ desires allows agents to recognize the situations where users need help. The paper briefly presents the ICagent development framework, describes the implementation of the interface agent, and discusses an example of the behavior of the agent during a collaboration session.     

Design of animated pedagogical agents—A look at their look

A well-established effect of animated agents in educational and other contexts is their potential to motivate and engage. “Increased motivation in users” is also one of the more frequent answers given to the question, “What is gained by adding an animated pedagogical agent to an intelligent tutoring system?”To further develop and exploit this potential, there are, however, several issues that need to be resolved. In this article we discuss the visual form and look of animated pedagogical agents. A survey is presented of how the area is approached (and, in particular, not approached) in research on animated pedagogical agents. Two possible reasons are proposed as to why visual form and look are so little addressed are also proposed. We also propose and discuss some key aspects of look that merit a systematic approach in future research.The main thesis of the paper is that users’ visuo-aesthetic experience of animated pedagogical agents is too important with respect to the goals to motivate and engage, to be treated as a secondary issue. We do not deny that there are other pressing and fundamental issues that need to be solved, such as those concerning the content of the support and the competence level of agents, as well as various design elements that can contribute to making animated agents lifelike. But we argue that visual rendering issues are pressing and need to be seriously addressed as well.     

BDI agent testability revisited

Agent-based systems are deployed to solve a wide range of problems in a wide range of domains. Before software is deployed, it is important to obtain assurance that it will function correctly. Traditionally, this assurance is obtained by testing. However, there is an intuition that agents exhibit more complex behaviour than traditional software, which raises the question: how testable are agent systems? We focus on BDI agent programs, and analyse their testability with respect to the all edges test adequacy criterion (also known as “branch coverage”). Our results augment earlier results that considered the all paths criterion to provide a richer and more nuanced understanding of the testability of BDI agents. We show that the number of tests required with respect to the all edges criterion is much lower than that required with respect to the all paths criterion. We also show that, as for the previous analysis, BDI programs are harder to test than equivalently-sized procedural programs, even if exception handling is introduced. Overall, our conclusions lend strength to the earlier work, and motivate the need for work on formal methods for agent systems.     

STRATUM: A METHODOLOGY FOR DESIGNING HEURISTIC AGENT NEGOTIATION STRATEGIES

Automated negotiation is a powerful (and sometimes essential) means for allocating resources among self-interested autonomous software agents. A key problem in building negotiating agents is the design of the negotiation strategy, which is used by an agent to decide its negotiation behavior. In complex domains, there is no single, obvious optimal strategy. This has led to much work on designing heuristic strategies, where agent designers usually rely on intuition and experience. In this article, we introduce STRATUM, a methodology for designing strategies for negotiating agents. The methodology provides a disciplined approach to analyzing the negotiation environment and designing strategies in light of agent capabilities and acts as a bridge between theoretical studies of automated negotiation and the software engineering of negotiation applications. We illustrate the application of the methodology by characterizing some strategies for the Trading Agent Competition and for argumentation-based negotiation.     

The Comprehensibility Theorem and the Foundations of Artificial Intelligence

Problem-solving software that is not-necessarily infallible is central to AI. Such software whose correctness and incorrectness properties are deducible by agents is an issue at the foundations of AI. The Comprehensibility Theorem, which appeared in a journal for specialists in formal mathematical logic, might provide a limitation concerning this issue and might be applicable to any agents, regardless of whether the agents are artificial or natural. The present article, aimed at researchers interested in the foundations of AI, addresses many questions related to that theorem, including differences between it and results of Gödel and Turing that have sometimes played key roles in Minds and Machines articles. This study also suggests that—if one is willing to assume a thesis due to Donald Knuth—the Comprehensibility Theorem is the first mathematical theorem implying the impossibility of any AI agent or natural agent—including a not-necessarily infallible human agent—satisfying a rigorous and deductive interpretation of the self-comprehensibility challenge. Some have pointed out the difficulty of self-comprehensibility, even according to presumably a less rigorous interpretation. This includes Socrates, who considered it to be among the most important of intellectual tasks. Self-comprehensibility in some form might be essential for a kind of self-reflection useful for self-improvement that might enable some agents to increase their success. We use the methods of applied mathematics, rather than philosophy, although some topics considered could be of interest to philosophers.     

Mixed-Initiative Issues in an Agent-Based Meeting Scheduler

This paper concerns mixed-initiative interaction between users and agents. After classifying agents according to their task and their interactivity with the user, the critical aspects of delegation-based interaction are outlined. Then MASMA, an agent system for distributed meeting scheduling, is described, and the solutions developed to control interaction are explained in detail. The issues addressed concern: the agent capability of adapting its behavior to the user it is supporting; the solution adopted to control the shift of initiative between personal agents, their users and other agents in the environment; the availability of features, e.g. the inspection mechanism, that endow the user with a further level of control to enhance his sense of trust in the agent.     

Social decisions and fairness change when people’s interests are represented by autonomous agents

There has been growing interest on agents that represent people’s interests or act on their behalf such as automated negotiators, self-driving cars, or drones. Even though people will interact often with others via these agent representatives, little is known about whether people’s behavior changes when acting through these agents, when compared to direct interaction with others. Here we show that people’s decisions will change in important ways because of these agents; specifically, we showed that interacting via agents is likely to lead people to behave more fairly, when compared to direct interaction with others. We argue this occurs because programming an agent leads people to adopt a broader perspective, consider the other side’s position, and rely on social norms—such as fairness—to guide their decision making. To support this argument, we present four experiments: in Experiment 1 we show that people made fairer offers in the ultimatum and impunity games when interacting via agent representatives, when compared to direct interaction; in Experiment 2, participants were less likely to accept unfair offers in these games when agent representatives were involved; in Experiment 3, we show that the act of thinking about the decisions ahead of time—i.e., under the so-called “strategy method”—can also lead to increased fairness, even when no agents are involved; and, finally, in Experiment 4 we show that participants were less likely to reach an agreement with unfair counterparts in a negotiation setting. We discuss theoretical implications for our understanding of the nature of people’s social behavior with agent representatives, as well as practical implications for the design of agents that have the potential to increase fairness in society.     

Dynamic Epistemic Logic for Implicit and Explicit Beliefs

Epistemic logic with its possible worlds semantic model is a powerful framework that allows us to represent an agent’s information not only about propositional facts, but also about her own information. Nevertheless, agents represented in this framework are logically omniscient: their information is closed under logical consequence. This property, useful in some applications, is an unrealistic idealisation in some others. Many proposals to solve this problem focus on weakening the properties of the agent’s information, but some authors have argued that solutions of this kind are not completely adequate because they do not look at the heart of the matter: the actions that allow the agent to reach such omniscient state. Recent works have explored how acts of observation, inference, consideration and forgetting affect an agent’s implicit and explicit knowledge; the present work focuses on acts that affect an agent’s implicit and explicit beliefs. It starts by proposing a framework in which these two notions can be represented, and then it looks into their dynamics, first by reviewing the existing notion of belief revision, and then by introducing a rich framework for representing diverse forms of inference that involve both knowledge and beliefs.     

Active tracking and pursuit under different levels of occlusion: a two-layer approach

We present an algorithm for real-time, robust, vision-based active tracking and pursuit. The algorithm was designed to overcome problems arising from active vision-based pursuit, such as target occlusion. Our method employs two layers to deal with occlusions of different lengths. The first layer is for short- or medium-term occlusions: those where a known method—such as mean shift combined with a Kalman filter—fails. For this layer we designed the hybrid filter for active pursuit (HAP). HAP utilizes a Kalman filter modified to respond to two different modes of action: one in which the target is positively identified and one in which the target identification is uncertain. For long-term occlusions we use the second layer. This layer is a decision algorithm that follows a learning procedure and is based on game theory-related reinforcement (Cesa-Bianchi and Lugosi, Prediction Learning and Games, 2006). The learning process is based on trial and error and is designed to perform adequately with a small number of samples. The algorithm produces a data structure that can be shared among agents or sent to a central control of a multi-agent system. The learning process is designed so that agents perform tasks according to their skills: an efficient agent will pursue targets while an inefficient agent will search for entering targets. These capacities make this system well suited for embedding in a multi-agent control system.     

The use of interface agents for email notification in critical incidents

This study reports on several typical scenarios of the use of email notification interface agents under the influence of critical incidents. An interface agent is a reactive, collaborative, and autonomous visual computational system, which communicates directly with a person offering assistance and advice in performing computer-related tasks. The critical incident technique was employed to survey the actual users of an interface agent-based email notification application. Respondents were asked to provide the last most significant either positive or negative incident of the usage of interface agents in their email application. They were also asked to offer recommendations for designers and marketers of this technology. Sixty critical incidents were obtained and analysed. With regards to positive-outcome situations, one representative scenario was constructed. With respect to the negative-outcome events, three distinct scenarios were identified. Based on the critical incident technique, it is concluded that users acknowledge the quality of an agent when it acts reliably, an agent's intrusive behaviour results in an immediate agent usage termination, operability issues sometimes force people to reject the technology, and users attempt to preserve the employment of an agent under the negative impacts of external factors. A number of other practical recommendations for manufacturers and marketers are also outlined.     

Flexible decision control in an autonomous trading agent

Modern electronic commerce creates significant challenges for decision-makers. The trading agent competition for supply-chain management (TAC SCM) is an annual competition among fully-autonomous trading agents designed by teams around the world. Agents attempt to maximize profits in a supply-chain scenario that requires them to coordinate Procurement, Production, and Sales activities in competitive markets. An agent for TAC SCM is a complex piece of software that must operate in a competitive economic environment. We report on results of an informal survey of agent design approaches among the competitors in TAC SCM, and then we describe and evaluate the design of our MinneTAC trading agent. We focus on the use of evaluators – configurable, composable modules for data analysis, modeling, and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports Sales and Procurement decisions, and how those decision process can be modified in useful ways by changing evaluator configurations.