Measuring the understanding between two agents through concept similarity

Two agents previously unknown to each other cannot communicate by exchanging concepts (nodes of their own ontology): they need to use a common communication language. If they do not use a standard protocol, most likely they use a natural language. The ambiguities of it, and the different concepts the agents possess, give rise to imperfect understanding among them: How closely concepts in ontology O_A map¹ to which of O_B? Can we measure these mismatches?Given a concept from ontology O_A, a method is provided to find the most similar concept in O_B, and to measure the similarity between both concepts. The paper also gives an algorithm to gauge du(A, B), the degree of understanding that agent A has about the ontology of B. The procedures use word comparison, since no agent (except the Very Wise Creature, VWC) can measure du directly. Examples are given.     

利用共用本体构造本体映射

一些代理机语言使得代理机能够理解代理机之间传递的信息的内容和潜在含义。交互中用到的信息载体是本体,由于本体是多种多样的,而且不同的本体对于同一个事物的描述是不一样的,这就阻碍了代理机之间的交互,这个问题被称作交互问题。文中提出共用本体的概念,建立通用的本体体系,首先建立本体与通用本体之间的映射,然后构建两个或两个以上异构本体之间的映射。实验表明,此方法不仅能够解决结构异构问题,也能解决语义异构问题。     

Concept learning games

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.     

Experimentation with local consensus ontologies with implications for automated service composition

Agent technologies represent a promising approach for the integration of interorganizational capabilities across distributed, networked environments. However, knowledge sharing interoperability problems can arise when agents incorporating differing ontologies try to synchronize their internal information. Moreover, in practice, agents may not have a common or global consensus ontology that will facilitate knowledge sharing and integration of functional capabilities. We propose a method to enable agents to develop a local consensus ontology during operation time as needed. By identifying similarities in the ontologies of their peer agents, a set of agents can discover new concepts/relations and integrate them into a local consensus ontology on demand. We evaluate this method, both syntactically and semantically, when forming local consensus ontologies with and without the use of a lexical database. We also report on the effects when several factors, such as the similarity measure, the relation search level depth, and the merge order, are varied. Finally, experimenting in the domain of agent-supported Web service composition, we demonstrate how our method allows us to successfully autonomously form service-oriented local consensus ontologies.     

Learning to Share Meaning in a Multi-Agent System

The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's common ontology paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.     

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.     

一种Agent通信中逻辑意外信息转换方法

在开放动态多Agent网络环境中,不同平台下的移动Agent希望就某个领域的问题进行通信,就需要避免出现逻辑意外现象,也就是必须使描述这一领域的术语取得一致.给出一个具有协作性和社会性的通信方法LOSCM(layer ontology services communication model).LOSCM具有以下特点:1)Agent通信实体中包含的概念采用本体方法进行描述,对本体作分层松散的社会性管理.2)假设本体不隶属于公共数据源且相互之间也没有公共本体,LOSCM给出一种基于概念替代遗失度和概念间替代相关度的有效的分层树状转换方法,有效避免了逻辑意外,确保了通信实体概念的正确理解,解决了在概念转换时的一致性问题.     

Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data

This paper presents an ontology-driven approach for spatial database enrichment in support of map generalisation. Ontology-driven spatial database enrichment is a promising means to provide better transparency, flexibility and reusability in comparison to purely algorithmic approaches. Geographic concepts manifested in spatial patterns are formalised by means of ontologies that are used to trigger appropriate low level pattern recognition techniques. The paper focuses on inference in the presence of vagueness, which is common in definitions of spatial phenomena, and on the influence of the complexity of spatial measures on classification accuracy. The concept of the English terraced house serves as an example to demonstrate how geographic concepts can be modelled in an ontology for spatial database enrichment. Owing to their good integration into ontologies, and their ability to deal with vague definitions, supervised Bayesian inference is used for inferring complex concepts. The approach is validated in experiments using large vector datasets representing buildings of four different cities. We compare classification results obtained with the proposed approach to results produced by a more traditional ontology approach. The proposed approach performed considerably better in comparison to the traditional ontology approach. Besides clarifying the benefits of using ontologies in spatial database enrichment, our research demonstrates that Bayesian networks are a suitable method to integrate vague knowledge about conceptualisations in cartography and GIScience.     

Ontology-based user profile learning

Personal agents gather information about users in a user profile. In this work, we propose a novel ontology-based user profile learning. Particularly, we aim to learn context-enriched user profiles using data mining techniques and ontologies. We are interested in knowing to what extent data mining techniques can be used for user profile generation, and how to utilize ontologies for user profile improvement. The objective is to semantically enrich a user profile with contextual information by using association rules, Bayesian networks and ontologies in order to improve agent performance. At runtime, we learn which the relevant contexts to the user are based on the user’s behavior observation. Then, we represent the relevant contexts learnt as ontology segments. The encouraging experimental results show the usefulness of including semantics into a user profile as well as the advantages of integrating agents and data mining using ontologies.     

A methodology for constructing of philosophy ontology based on philosophical texts

An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.     

Hierarchical multi-agent reinforcement learning

In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL. In this framework, agents are cooperative and homogeneous (use the same task decomposition). Learning is decentralized, with each agent learning three interrelated skills: how to perform each individual subtask, the order in which to carry them out, and how to coordinate with other agents. We define cooperative subtasks to be those subtasks in which coordination among agents significantly improves the performance of the overall task. Those levels of the hierarchy which include cooperative subtasks are called cooperation levels. A fundamental property of the proposed approach is that it allows agents to learn coordination faster by sharing information at the level of cooperative subtasks, rather than attempting to learn coordination at the level of primitive actions. We study the empirical performance of the Cooperative HRL algorithm using two testbeds: a simulated two-robot trash collection task, and a larger four-agent automated guided vehicle (AGV) scheduling problem. We compare the performance and speed of Cooperative HRL with other learning algorithms, as well as several well-known industrial AGV heuristics. We also address the issue of rational communication behavior among autonomous agents in this paper. The goal is for agents to learn both action and communication policies that together optimize the task given a communication cost. We extend the multi-agent HRL framework to include communication decisions and propose a cooperative multi-agent HRL algorithm called COM-Cooperative HRL. In this algorithm, we add a communication level to the hierarchical decomposition of the problem below each cooperation level. Before an agent makes a decision at a cooperative subtask, it decides if it is worthwhile to perform a communication action. A communication action has a certain cost and provides the agent with the actions selected by the other agents at a cooperation level. We demonstrate the efficiency of the COM-Cooperative HRL algorithm as well as the relation between the communication cost and the learned communication policy using a multi-agent taxi problem.     

一种改进的本体相似度综合映射方法研究

为了解决本体映射方法中计算量大、方法单一的问题,提出本体相似度综合映射方法。首先分解合适的本体,将规模比较大的本体分解为小本体,以降低映射计算的时间复杂度;然后根据本体映射的启发规则筛选出候选概念集,对候选概念集进行基于世界知识体系的本体概念相似度计算,再进行语义相似度和结构相似度计算,并把这3种不同算法得到的相似度值进行加权综合,给出最终的本体概念相似度值,再根据该值进行本体映射;最后通过设计实验来验证算法的正确性与有效性,结果表明本方法能在提高映射效率的同时保证良好的查询效果。     

Toward analyzing impact of disjoint axioms for merging heterogeneous ontologies

In recent years, the question on Automatic Ontology Merging (AOM) become challenging to address for the researchers. Our research and development for the Disjoint Knowledge Perservation based Automatic Ontology Merging (DKP-AOM) is a milestone in the same direction. This paper provides a more specific discussion about disjoint knowledge axioms in DKP-AOM and makes an assessment of our merge algorithm that looks-up within disjoint partitions of concept hierarchies of ontologies. The significant use of disjoint knowledge is corroborated by testing conference and vertebrate ontologies. The results reveal that disjoint knowledge axioms help identifying initial inaccurate mappings and remove ambiguity when the concept with same symbolic identifier has a different meaning in different local ontologies in the process of ontology merging. Disjoint axioms separate the knowledge in distinct chunks and enable concept matching within the boundaries of sub-hierarchies of the entire ontology concept hierarchy. While finding matches between concepts of ontologies, disjoint partitions with the disjoint knowledge about concepts in source ontologies minimize the search space and reduce the runtime complexity of ontology merging. We also discuss encouraging results obtained by our DKP-AOM system within the OAEI 2015 campaign.     

从Web中提取中文本体非分类关系的方法

为了有效地学习本体中的非分类关系以协助知识工程师构建领域本体,提出了一种在中文领域本体学习环境中自动获取概念之间非分类关系的方法,该方法以Web为数据源来提取候选关系并计算信息分布的统计特征,把动词作为发现非分类关系的中心点,把领域相关的动词作为种子来检索领域相关概念并用来标记相应的关系.该方法的学习结果是一个多级分类关系和非分类关系组成的语义体系.最后,通过对"癌"本体相应关系的提取及其性能分析,表明了该方法的学习结果和性能.     

A HYBRID METHOD FOR FUZZY ONTOLOGY INTEGRATION

The main contribution of this work consists of combining a heuristic method for propagation of matchable concepts and using consensus techniques for conflict resolution for fuzzy ontology integration. Two central observations behind this approach are as follows: (1) if two concepts across different source ontologies equivalently match each other, then their neighboring concepts will be often matched as well; and (2) conflicts regarding integration of multiple ontologies can be resolved by creating a consensus among the conflict ontological entities. The key idea of the first observation is to start from an aligned pair of concepts (called medoids) to determine so-called potentially common parts to provide additional suggestions for possible matching concepts. This approach is used to obtain pairs of matchable concepts and to avoid pairs of mismatching concepts. On the other hand, the second observation is used to discover a new merged concept from matched concepts by making a consensus among conflict ontological entities. This idea is to determine the best representative as the merged version of the component ones. A combination of both observations for fuzzy ontology integration is a significant contribution of this work. The results of the experiments imply that the proposed approach is effective with regard to both completeness and accuracy.     

A hybrid evolutionary approach for solving the ontology alignment problem

Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so‐called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high‐quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms. © 2012 Wiley Periodicals, Inc.     

基于语义近似逼近技术的多代理协作

本体在多代理系统中起着重要的作用,它提供和定义了一个共享的语义词汇库。然而,在现实的多代理通讯的过程中,两个代理共享完全相同的语义词汇库是几乎不可能的。因为信息不完整以及本体的异构等特性,一个代理只能部分理解另外一个代理所拥有的本体内容,这使得代理间的通讯非常困难。本文就是探索利用近似逼近技术实现基于部分共享分布式本体的多代理通讯,从而实现多代理之间的协作查询。我们使用基于OWLweb本体语言的描述逻辑来描述分布式本体的近似查询技术。最终我们也开发了基于语义近似逼近方法的一个多代理协调查询系统。     

Exploring selfish reinforcement learning in repeated games with stochastic rewards

In this paper we introduce a new multi-agent reinforcement learning algorithm, called exploring selfish reinforcement learning (ESRL). ESRL allows agents to reach optimal solutions in repeated non-zero sum games with stochastic rewards, by using coordinated exploration. First, two ESRL algorithms for respectively common interest and conflicting interest games are presented. Both ESRL algorithms are based on the same idea, i.e. an agent explores by temporarily excluding some of the local actions from its private action space, to give the team of agents the opportunity to look for better solutions in a reduced joint action space. In a latter stage these two algorithms are transformed into one generic algorithm which does not assume that the type of the game is known in advance. ESRL is able to find the Pareto optimal solution in common interest games without communication. In conflicting interest games ESRL only needs limited communication to learn a fair periodical policy, resulting in a good overall policy. Important to know is that ESRL agents are independent in the sense that they only use their own action choices and rewards to base their decisions on, that ESRL agents are flexible in learning different solution concepts and they can handle both stochastic, possible delayed rewards and asynchronous action selection. A real-life experiment, i.e. adaptive load-balancing of parallel applications is added.     

Expertness based cooperative Q-learning

By using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules from the other agents' knowledge for its own requirements. One possible way to do this is to have the learner assign some expertness values (intelligence level values) to the other agents and use their knowledge accordingly. Some criteria to measure the expertness of the reinforcement learning agents are introduced. Also, a new cooperative learning method, called weighted strategy sharing (WSS) is presented. In this method, each agent measures the expertness of its teammates and assigns a weight to their knowledge and learns from them accordingly. The presented methods are tested on two Hunter-Prey systems. We consider that the agents are all learning from each other and compare them with those who cooperate only with the more expert ones. Also, the effect of communication noise, as a source of uncertainty, on the cooperative learning method is studied. Moreover, the Q-table of one of the cooperative agents is changed randomly and its effects on the presented methods are examined.