Programming Rational Agents in a Modal Action Logic

In this paper we describe a language for reasoning about actions that can be used for modelling and for programming rational agents. We propose a modal approach for reasoning about dynamic domains in a logic programming setting. Agent behavior is specified by means of complex actions which are defined using modal inclusion axioms. The language is able to handle knowledge producing actions as well as actions which remove information. The problem of reasoning about complex actions with incomplete knowledge is tackled and the temporal projection and planning problems is addressed; more specifically, a goal directed proof procedure is defined, which allows agents to reason about complex actions and to generate conditional plans. We give a non-monotonic solution for the frame problem by making use of persistency assumptions in the context of an abductive characterization. The language has been used for implementing an adaptive web-based system.     

Abductive case‐based reasoning

This article introduces abductive case‐based reasoning (CBR) and attempts to show that abductive CBR and deductive CBR can be integrated in clinical process and problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction, and deductive CBR. This article also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations, and similarity metrics. The proposed approach demonstrates that the integration of deductive CBR and abductive CBR is of practical significance in problem solving such as system diagnosis and analysis, and will facilitate research of abductive CBR and deductive CBR. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 957–983, 2005.     

Generation and Selection of Abductive Explanations for Non-Omniscient Agents

Among the non-monotonic reasoning processes, abduction is one of the most important. Usually described as the process of looking for explanations, it has been recognized as one of the most commonly used in our daily activities. Still, the traditional definitions of an abductive problem and an abductive solution mention only theories and formulas, leaving agency out of the picture. Our work proposes a study of abductive reasoning from an epistemic and dynamic perspective. In the first part we explore syntactic definitions of both an abductive problem in terms of an agent’s information and an abductive solution in terms of the actions that modify the agent’s information. We look at diverse kinds of agents, including not only omniscient ones but also those whose information is not closed under logical consequence and those whose reasoning abilities are not complete. In the second part, we look at an existing logical framework whose semantic model allows us to interpret the previously stated formulas, and we define two actions that represent forms of abductive reasoning.     

Extending expressivity and flexibility of abductive logic programming

Real-world problems often require purely deductive reasoning to be supported by other techniques that can cope with noise in the form of incomplete and uncertain data. Abductive inference tackles incompleteness by guessing unknown information, provided that it is compliant with given constraints. Probabilistic reasoning tackles uncertainty by weakening the sharp logical approach. This work aims at bringing both together and at further extending the expressive power of the resulting framework, called Probabilistic Expressive Abductive Logic Programming (PEALP). It adopts a Logic Programming perspective, introducing several kinds of constraints and allowing to set a degree of strength on their validity. Procedures to handle both extensions, compatibly with standard abductive and probabilistic frameworks, are also provided.     

Reducing one class of machine learning algorithms to logical operations of plausible reasoning

The ways to transform a wide class of machine learning algorithms into processes of plausible reasoning based on known deductive and inductive rules of inference are shown. The employed approach to machine learning problems is based on the concept of a good classification (diagnostic) test for a given set of positive and negative examples. The problem of inferring all good diagnostic tests is to search for the best approximations of the given classification (partition or the partitioning) on the established set of examples. The theory of algebraic lattice is used as a mathematical language to construct algorithms of inferring good classification tests. The advantage of the algebraic lattice is that it is given both as a declarative structure, i.e., the structure for knowledge representation, and as a system of dual operations used to generate elements of this structure. In this work, algorithms of inferring good tests are decomposed into subproblems and operations that are the main rules of plausible human inductive and deductive reasoning. The process of plausible reasoning is considered as a sequence of three mental acts: implementing the rule of reasoning (inductive or deductive)with obtaining a new assertion, refining the boundaries of reasoning domain, and choosing a new rule of reasoning (deductive or inductive one).     

基于Agent的建模与仿真概述

基于Agent的建模与仿真(ABMS)是研究复杂系统的有效途径和建模仿真方法学,足当前最具有活力、有所突破的仿真方法学,已经成为系统仿真领域的一个新的研究方向.全面总结和阐述了基于Agent的建模与仿真的相关理论基础和概念,包括CAS理论,ABMS中Agent的概念、结构;阐述了ABMS的原理(简单规则导致复杂的行为)与研究步骤;总结了ABMS的主要应用领域,包括经济领域、社会科学领域和军事领域;慨述了ABMS软件开发平台和工具包,给出了ABMS开发方法;给出了互联网上关于ABMS的的资源链接.因而有利于全面认识、应用和研究ABMS.     

Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams

Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in individual decision making in multiagent settings face the task of having to reason about other agents’ actions, which may in turn involve reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. For the purposes of this study, individual, self-interested decision making in multiagent settings is modeled using interactive dynamic influence diagrams (I-DID). These are graphical models with the benefit that they naturally offer a factored representation of the problem, allowing agents to ascribe dynamic models to others and reason about them. We demonstrate that an implication of bounded, finitely-nested reasoning by a self-interested agent is that we may not obtain optimal team solutions in cooperative settings, if it is part of a team. We address this limitation by including models at level 0 whose solutions involve reinforcement learning. We show how the learning is integrated into planning in the context of I-DIDs. This facilitates optimal teammate behavior, and we demonstrate its applicability to ad hoc teamwork on several problem domains and configurations.     

Cooperative situation assessment in a maritime scenario

In large‐scale, complex domains such as space defense and security systems, situation assessment and decision making are evolving from centralized models to high‐level, net‐centric models. In this context, collaboration among the many actors involved in the situation assessment process is critical to achieve a prompt reaction as needed in the operational scenario. In this paper, we propose a multiagent‐based approach to situation assessment, where agents cooperate by sharing local information to reach a common and coherent assessment of situations. Specifically, we characterize situation assessment as a classification process based on OWL ontology reasoning, and we provide a protocol for cooperative multiagent situation assessment, which allows the agents to achieve coherent high‐level conclusions. We validate our approach in a real maritime surveillance scenario, where our prototype system effectively supports the user in detecting and classifying potential threats; moreover, our distributed solution performs comparably to a centralized method, while preserving independence of decision makers and dramatically reducing the amount of communication required. © 2012 Wiley Periodicals, Inc.     

协同设计中的多主体动态协调

确保多个主体协调一致的问题求解是协同设计系统性能好坏的重要标志。为此提出了一个面向黑板的混合推理主体结构。其中,基于事例推理实现多主体动态协调,用以解决由于主体间冲突所引起的系统控制问题。在考虑了已知事例属性个数以及与所求问题属性的相容数、匹配数的基础上,提出一事例匹配估算函数。     

Continual planning and acting in dynamic multiagent environments

In order to behave intelligently, artificial agents must be able to deliberatively plan their future actions. Unfortunately, realistic agent environments are usually highly dynamic and only partially observable, which makes planning computationally hard. For most practical purposes this rules out planning techniques that account for all possible contingencies in the planning process. However, many agent environments permit an alternative approach, namely continual planning, i.e. the interleaving of planning with acting and sensing. This paper presents a new principled approach to continual planning that describes why and when an agent should switch between planning and acting. The resulting continual planning algorithm enables agents to deliberately postpone parts of their planning process and instead actively gather missing information that is relevant for the later refinement of the plan. To this end, the algorithm explictly reasons about the knowledge (or lack thereof) of an agent and its sensory capabilities. These concepts are modelled in the planning language (MAPL). Since in many environments the major reason for dynamism is the behaviour of other agents, MAPL can also model multiagent environments, common knowledge among agents, and communicative actions between them. For Continual Planning, MAPL introduces the concept of of assertions, abstract actions that substitute yet unformed subplans. To evaluate our continual planning approach empirically we have developed MAPSIM, a simulation environment that automatically builds multiagent simulations from formal MAPL domains. Thus, agents can not only plan, but also execute their plans, perceive their environment, and interact with each other. Our experiments show that, using continual planning techniques, deliberate action planning can be used efficiently even in complex multiagent environments.     

基于溯因推理的主体协商

为了能够进行有效的协商，主体应当提高通信的效率。为此，接收者可以对发送者的当时的意识状态进行推测，这可以通过溯因推理实现。该文提出了一个基于溯因推理的主体协商模型，是对Parsons的基于论据的协商模型的改进。     

Reasoning paradigms in legal decision support systems

In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.     

Globally linear connection method

To model in a formal system the remarkable ability of human agents to reason about situations, actions, and causality has always been a major research goal in Intellectics. Most of the work towards this goal is based on the situation calculus which, however, has the disadvantage that it requires either to state frame axioms or to use non-monotonic logic and a commonsense law of inertia. A deductive approach which does not show this disadvantage is the linear connection method whose key idea is to treat facts about a situation as resources which can be consumed and produced by actions. It was shown that this approach properly handles planning problems which only allow deterministic actions, i.e. actions which are not allowed to have several alternative effects. In this paper we extend and revise the linear connection method to overcome this restriction. Stefan Brüning, Ph.D.: He received a M. Sc. in Computer Science from the Technical University of Darmstadt in 1992. From 1992 to present he has been a research officer in the Intellectics group at the same university where he received his Ph. D. in 1995. His thesis was entitled “Techniques for Avoiding Redundancy in Theorem Proving Based on the Connection Method”. His current research interests are in the development of efficient calculi for different kinds of deductive tasks, such as deductive planning or default reasoning.     

Model-based reasoning

In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper contextual and social support. More specifically, we first will identify some key elements of the structure and function of mental models in contrast to schemas. Next, these key elements of modeling will be used to generate some conjectures about the foundations of model-based reasoning. In the next section, we will describe the learning-dependent progression of mental models as a suitable approach for understanding the basics of deductive and inductive reasoning based on models as “tools for thought.” The rationale of mental models as tools for reasoning will be supported by empirical research to be described in a particular section of this paper. Finally, we will turn to the instructional implications of model-based reasoning by discussing appropriate instructional methods to affect the construction of mental models for performing deductive and inductive reasoning.     

Reasoning about Commitments in the Event Calculus: An Approach for Specifying and Executing Protocols

Commitments among agents are widely recognized as an important basis for organizing interactions in multiagent systems. We develop an approach for formally representing and reasoning about commitments in the event calculus. We apply and evaluate this approach in the context of protocols, which represent the interactions allowed among communicating agents. Protocols are essential in applications such as electronic commerce where it is necessary to constrain the behaviors of autonomous agents. Traditional approaches, which model protocols merely in terms of action sequences, limit the flexibility of the agents in executing the protocols. By contrast, by formally representing commitments, we can specify the content of the protocols through the agents' commitments to one another. In representing commitments in the event calculus, we formalize commitment operations and domain-independent reasoning rules as axioms to capture the evolution of commitments. We also provide a means to specify protocol-specific axioms through the agents' actions. These axioms enable agents to reason about their actions explicitly to flexibly accommodate the exceptions and opportunities that may arise at run time. This reasoning is implemented using an event calculus planner that helps determine flexible execution paths that respect the given protocol specifications.     

Modular fuzzy-reinforcement learning approach with internal model capabilities for multiagent systems.

To date, many researchers have proposed various methods to improve the learning ability in multiagent systems. However, most of these studies are not appropriate to more complex multiagent learning problems because the state space of each learning agent grows exponentially in terms of the number of partners present in the environment. Modeling other learning agents present in the domain as part of the state of the environment is not a realistic approach. In this paper, we combine advantages of the modular approach, fuzzy logic and the internal model in a single novel multiagent system architecture. The architecture is based on a fuzzy modular approach whose rule base is partitioned into several different modules. Each module deals with a particular agent in the environment and maps the input fuzzy sets to the action Q-values; these represent the state space of each learning module and the action space, respectively. Each module also uses an internal model table to estimate actions of the other agents. Finally, we investigate the integration of a parallel update method with the proposed architecture. Experimental results obtained on two different environments of a well-known pursuit domain show the effectiveness and robustness of the proposed multiagent architecture and learning approach.     

一种基于案例推理的多agent 强化学习方法研究

提出一种基于案例推理的多agent 强化学习方法．构建了系统策略案例库,通过判断agent 之间的协作 关系选择相应案例库子集．利用模拟退火方法从中寻找最合适的可再用案例策略,agent 按照案例指导执行动作选 择．在没有可用案例的情况下,agent 执行联合行为学习（JAL）．在学习结果的基础上实时更新系统策略案例库．追 捕问题的仿真结果表明所提方法明显提高了学习速度与收敛性．     

Abductive science inquiry using mobile devices in the classroom

Recent advancements in digital technology have attracted the interest of educators and researchers to develop technology-assisted inquiry-based learning environments in the domain of school science education. Traditionally, school science education has followed deductive and inductive forms of inquiry investigation, while the abductive form of inquiry has previously been sparsely explored in the literature related to computers and education. We have therefore designed a mobile learning application ‘ThinknLearn’, which assists high school students in generating hypotheses during abductive inquiry investigations. The M3 evaluation framework was used to investigate the effectiveness of using ‘ThinknLearn’ to facilitate student learning. The results indicated in this paper showed improvements in the experimental group's learning performance as compared to a control group in pre-post tests. In addition, the experimental group also maintained this advantage during retention tests as well as developing positive attitudes toward mobile learning.