EKEMAS,an agent-based geo-simulation framework to support continual planning in the real-word

In this paper, we propose an agent-based geo-simulation framework EKEMAS to assist human planners when planning under strong spatial constraints in a real large-scale space. The approach consists in drawing a parallel between the real environment (for example, a forest in fire) and the simulated environment based on GIS data. This virtual environment uses software agents which are aware of the space and equipped with advanced spatial reasoning capabilities. In addition, we suggest some enhancements for the Continual Planning approach. Our aim is to demonstrate how EKEMAS, when coupled with a continual planning approach and agent’s spatial reasoning capabilities, can assist human planners overcoming obstacles related to real world constraints: dynamic, uncertain, and spatially constrained environment. We illustrate this idea on the forest firefighting problem and we use MAGS as a simulation platform and Prometheus as a fire simulator. Finally, and since plans in the studied case (wildfire fighting) are mainly paths, we also propose a new approach based on agent geo-simulation in order to solve particular Pathfinding problems.     

Autoepistemic logic of first order and its expressive power

We study the expressive power of first-order autoepistemic logic. We argue that full introspection of rational agents should be carried out by minimizing positive introspection and maximizing negative introspection. Based on full introspection, we propose the maximal well-founded semantics that characterizes autoepistemic reasoning processes of rational agents, and show that breadth of the semantics covers all theories in autoepistemic logic of first order, Moore's AE logic, and Reiter's default logic. Our study demonstrates that the autoepistemic logic of first order is a very powerful framework for nonmonotonic reasoning, logic programming, deductive databases, and knowledge representation.This research is partially supported by NSERC grant OGP42193.     

基于自我意识的Agent通信语言研究

Agent间的通信目的是不断完善其自我意识,Agent间的通信内容是Agent对其自我意识的解释,Agent间的通信过程是Agent在其自我意识上的推理。在研究经典Agent通信语言的基础上,结合知识类别、自我意识、语气等相关研究成果,以能描述语义的互表性、模糊性、动态性和自我意识性的动态本体描述语言作为通信内容的表示工具,设计了一个基于自我意识的Agent通信语言,给出了该语言的消息结构和语用词,并通过实例来解释Agent间的通信过程是Agent在其自我意识上的推理。     

The dynamics of default reasoning

In this paper we study default reasoning from a dynamic, agent-oriented, semantics-based point of view. In a formal framework used to specify and to reason about rational agents, we introduce actions that model the (attempted) jumping to conclusions that is a fundamental part of reasoning by default. Application of such an action consists of three parts. First it is checked whether the formula that the agent tries to jump to is a default, thereafter it is checked whether the default formula can consistently be incorporated by the agent, and if this is the case the formula is included in the agent's beliefs. As for all actions in our framework, we define the ability and opportunity of agents to apply these actions, and the states of affairs following application. To formalise formulae being defaults, we introduce the modality of common possibility. This modality is related to, but not reducible to, the notions of common knowledge and ‘everybody knows’-knowledge. To model the qualitative difference that exists between hard, factual knowledge and beliefs derived by default, we employ different modalities to represent these concepts, thus combining knowledge, beliefs, and defaults in one framework. Based on the concepts used to model the default reasoning of agents, we look into the dynamics of the supernormal fragment of default logic. We show in particular that by sequences of jumps to conclusions agents can end up with extensions in the sense of default logic of their belief.     

Massive deliberation [Web agents]

Agents are proliferating on the Web, making it conceivable that their collective reasoning ability might someday be harnessed for robust decision-making. The hope is that massive deliberation power can soon help solve problems that require knowledge, reasoning, and intelligence. Until recently, working individually or in small groups, agents across the Web could barely communicate and could only reason under conditions of severely bounded rationality. Projects such as Agentcities showed that widespread heterogeneous agents could collaborate on specific predefined tasks and provide diverse agent-based services. When the tasks are dynamic, of long duration, and ill defined, however, success requires planning that is continual, distributed, and accounts for the social fabric into which the plans and their execution must fit. The authors discusses distributed planning and societal agents.     

Minimal Belief and Negation as Failure in Multi-Agent Systems

We propose an epistemic, nonmonotonic approach to the formalization of knowledge in a multi-agent setting. From the technical viewpoint, a family of nonmonotonic logics, based on Lifschitz's modal logic of minimal belief and negation as failure, is proposed, which allows for formalizing an agent which is able to reason about both its own knowledge and other agents' knowledge and ignorance. We define a reasoning method for such a logic and characterize the computational complexity of the major reasoning tasks in this formalism. From the practical perspective, we argue that our logical framework is well-suited for representing situations in which an agent cooperates in a team, and each agent is able to communicate his knowledge to other agents in the team. In such a case, in many situations the agent needs nonmonotonic abilities, in order to reason about such a situation based on his own knowledge and the other agents' knowledge and ignorance. Finally, we show the effectiveness of our framework in the robotic soccer application domain.     

Assumption‐Based Argumentation Equipped with Preferences and its Application to Decision Making,Practical Reasoning,and Epistemic Reasoning

The existing approaches that map the given explicit preferences into standard assumption‐based argumentation (ABA) frameworks reveal some difficulties such as generating a huge number of rules. To overcome them, we present an assumption‐based argumentation framework equipped with preferences (p_ABA). It increases the expressive power of ABA by incorporating preferences between sentences into the framework. The semantics of p_ABA is given by extensions, which are maximal among extensions of ABA with regard to the extension ordering “lifted” from the given sentence ordering. As a theoretical contribution of this study, we show that prioritized logic programming can be formulated as a specific form of p_ABA. The advantage of our approach is that not only does p_ABA enable us to express different kinds of preferences such as preferences over rules, over goals, or over decisions by means of sentence orderings but we can also successfully obtain solutions from extensions of the p_ABA expressing the respective knowledge for various applications such as epistemic reasoning, practical reasoning, and decision making with preferences in a uniform and domain‐independent way without suffering from difficulties of the existing approaches.     

Reasoning About Agent Types and the Hardest Logic Puzzle Ever

In this paper, we first propose a simple formal language to specify types of agents in terms of necessary conditions for their announcements. Based on this language, types of agents are treated as ‘first-class citizens’ and studied extensively in various dynamic epistemic frameworks which are suitable for reasoning about knowledge and agent types via announcements and questions. To demonstrate our approach, we discuss various versions of Smullyan’s Knights and Knaves puzzles, including the Hardest Logic Puzzle Ever (HLPE) proposed by Boolos (in Harv Rev Philos 6:62–65, 1996). In particular, we formalize HLPE and verify a classic solution to it. Moreover, we propose a spectrum of new puzzles based on HLPE by considering subjective (knowledge-based) agent types and relaxing the implicit epistemic assumptions in the original puzzle. The new puzzles are harder than the previously proposed ones in the literature, in the sense that they require deeper epistemic reasoning. Surprisingly, we also show that a version of HLPE in which the agents do not know the others’ types does not have a solution at all. Our formalism paves the way for studying these new puzzles using automatic model checking techniques.     

A Constraint-Based Reasoning Framework for Behavioural Animation

Behaviour is a reflection of a reasoning process that must deal with constraints imposed by an external environment, internal knowledge and physical structure. This paper proposes a framework for behavioural animation that is based on the next generation of object-oriented, constraint-based expert systems technology, and applies a control structure of knowledge agents and knowledge units to determine the behaviour of objects to be animated. Knowledge agents are responsible for planning, plan implementation and information extraction from the environment. The activity of an agent is dependent on the knowledge units ascribed to them by the animator. The interaction between agents and knowledge units is resolved by the reasoning engine, and thus, influences the eventual motion displayed. An example given is NSAIL, a pilot implementation using the model-based ECHIDNA constraint logic programming shell. With this approach, the motion for a sailing scenario and other behavioural domains can be specified at a high level through the characterization of the knowledge agents.     

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.     

COORDINATION DIAGNOSTIC ALGORITHMS FOR TEAMS OF SITUATED AGENTS: SCALING UP

Agents in a team should be in agreement. Unfortunately, they may come to disagree due to sensor uncertainty, intermittent communication failures, etc. Once a disagreement occurs, the agents should detect and diagnose the disagreement. Current diagnostic techniques do not scale well with the number of agents, as they have high communication and computation complexity. We present novel techniques that enable scalability in three ways. First, we use communications early in the diagnostic process to stave off unneeded reasoning, which ultimately leads to unneeded communications. Second, we use light‐weight (and inaccurate) behavior recognition to focus the diagnostic reasoning on beliefs of agents that might be in conflict. Finally, we propose diagnosing only to a limited number of representative agents (instead of all the agents). We examine these techniques in large‐scale teams of situated agents in two domains and show that combining the techniques produces a diagnostic process that is highly scalable in both communication and computation.     

ANGLE: An autonomous, normative and guidable agent with changing knowledge

Some emerging computing systems (especially autonomic computing systems) raise several challenges to autonomous agents, including (1) how to reflect the dynamics of business requirements, (2) how to coordinate with external agents with sufficient level of security and predictability, and (3) how to perform reasoning with dynamic and incomplete knowledge, including both informational knowledge (observations) and motivational knowledge (for example, policy rules and contract rules). On the basis of defeasible logic and argumentation, this paper proposes an autonomous, normative and guidable agent model, called ANGLE, to cope with these challenges. This agent is established by combining beliefs-desires-intentions (BDI) architecture with policy-based method and the mechanism of contract-based coordination. Its architecture, knowledge representation, as well as reasoning and decision-making, are presented in this paper. ANGLE is characteristic of the following three aspects. First, both its motivational knowledge and informational knowledge are changeable, and allowed to be incomplete, inconsistent/conflicting. Second, its knowledge is represented in terms of extended defeasible logic with modal operators. Different from the existing defeasible theories, its theories (including belief theory, goal theory and intention theory) are dynamic (called dynamic theories), reflecting the variations of observations and external motivational knowledge. Third, its reasoning and decision-making are based on argumentation. Due to the dynamics of underlying theories, argument construction is not a monotonic process, which is different from the existing argumentation framework where arguments are constructed incrementally.     

Reasoning tractably about explicit belief: A model‐theoretic approach

Existing epistemic logics such as the logic of implicit and explicit belief and the logic of awareness adopt a deductive‐theoretic approach for characterizing belief. In this approach, an agent represents the state of the world with a conjunction of axioms in its knowledge base (KB) and evaluates queries by trying to prove or disprove that they follow from KB. This paper presents a multivalued epistemic logic (MEL) that allows agents to reason both deductively and model theoretically about implicit and explicit belief. By characterizing an agent's KB with a class of finite models, the set of formulas that an agent believes can be determined by checking their validity in all these models. This rests on the fact that MEL has a complete axiomatization (sentences that are true in all these models will also be provable). In this paper, the soundness, completeness, and decidability of MEL are proven. Furthermore, a polynomial time model‐checking algorithm for determining the satisfiability of a sentence at a particular state in a given model of MEL is also presented. © 2000 John Wiley & Sons, Inc.     

Scaffolding student teachers' information‐seeking behaviours with a network‐based tutoring system

Student teachers' instructional planning requires them to regulate certain aspects of their own learning while designing lessons. The aim of this study is to support student teachers' self‐regulated learning through the convergence effect, where network‐based tutors are designed to optimize system recommendations of online resources based on information‐seeking behaviours. A total of 68 student teachers were randomly assigned to either a dynamic or static version of nBrowser, which converged a network or not towards an optimal configuration. The structural equation model suggests that student teachers spent less time during the learning session using the dynamic version of nBrowser. Although student teachers were found to be more efficient in seeking and acquiring information and reported knowledge gains, they failed to perform better than those assigned to the static condition on the lesson plan design task. We discuss the implications for the convergence effect in the context of network‐based tutors.     

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.     

Playing MUSIC — building context‐aware and self‐adaptive mobile applications

Although the idea of context‐awareness was introduced almost two decades ago, few mobile software applications are available today that can sense and adapt to their run‐time environment. The development of context‐aware and self‐adaptive applications is complex and few developers have experience in this area. On the basis of several demonstrators built by the joint European research project MUSIC, this paper describes typical context and adaptation features relevant for the development of context‐aware and self‐adaptive mobile applications. We explain how the demonstrators were realised using the open‐source platform MUSIC and present the feedback of the developers of these demonstrators. The main contribution of this paper is to show how the development complexity of context‐aware and self‐adaptive mobile applications can be mastered by using an adaptation framework such as MUSIC. Copyright © 2012 John Wiley & Sons, Ltd.     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

Cooperation and Competition in ALIAS: A Logic Framework for Agents that Negotiate

This paper presents ALIAS, an agent architecture based on intelligent logic agents, where the main form of agent reasoning is abduction. The system is particularly suited for solving problems where knowledge is incomplete, where agents may need to make reasonable hypotheses about the problem domain and other agents, and where the raised hypotheses have to be consistent for the overall set of agents. ALIAS agents are pro-active, exhibiting a goal-directed behavior, and autonomous, since each one can solve problems using its own private knowledge base. ALIAS agents are also social, because they are able to interact with other agents, in order to cooperatively solve problems. The coordination mechanisms are modeled by means of LAILA, a logic-based language which allows to express intra-agent reasoning and inter-agent coordination. As an application, we show how LAILA can be used to implement inter-agent dialogues, e.g., for negotiation. In particular, LAILA is well-suited to coordinate the process of negotiation aimed at exchanging resources between agents, thus allowing them to execute the plans to achieve their goals.