Promoting academically productive talk with conversational agent interventions in collaborative learning settings

Research on computer-supported collaborative learning (CSCL) and conversational pedagogical agents has strongly emphasized the value of providing dynamic dialogue support for learners working together to accomplish a certain task. Recently, on the basis of the classroom discourse framework of Academically Productive Talk (APT), a flexible form of conversational agent support has emerged employing APT-based intervention methods so as to stimulate pedagogically beneficial conversational interactions among learning partners. This paper investigates the impact of an APT-based Linking Contributions (LC) intervention mode implemented by a conversational agent in the context of a collaborative activity in higher education. This type of agent interventions encourages students to explicitly externalize their reasoning on important domain concepts building upon the contributions of their partners. Forty-three (43) students collaborated in small groups using a prototype CSCL system to accomplish three different tasks in the domain of Multimedia Learning. Groups were randomly assigned to the treatment or the control condition. In the treatment condition, a conversational agent participated in students' dialogues making LC mode interventions. In the control condition, students discussed without the agent intervening. The results of the study illustrated that the students in the treatment condition engaged in a more productive dialogue demonstrating increased explicit reasoning throughout the collaborative activity. Furthermore, it was shown that the students in the treatment condition outperformed the control students in various measures on knowledge acquisition. Evidence also suggests that students' enhanced learning performance was mediated by the positive effect of the agent intervention mode on students' argumentation. Overall, this study provides insights into how the use of a configurable conversational agent displaying unsolicited LC interventions during students' discourse can be beneficial to collaborative learning.     

基于机器学习的动态信誉评估模型研究

为在开放网络环境中建立资源消费者(用户)和资源提供者(主机)之间的信任关系,提出基于机器学习的动态信誉评估模型 .模型中用户的信誉级别可以根据其行为和一些其他监测数据动态变化,而资源的信誉级别也可以根据用户对资源所提供服务的评价动态变化 .给出了用于生成评估规则和信誉级别的模糊信誉级别评估算法(FTEA),算法采用基于规则的机器学习方法,具有从大量输入数据中自学习以获取评估规则的能力 .实验结果表明,1000组输入数据能够生成理想的规则库,并且算法执行时间随输入判定因素数目成指数形式增长,因此需要选择5～6个因素和1000个左右的样本数据以进行系统实现 .     

A multicontext architecture for formalizing complex reasoning

We propose multicontext systems (MC systems) as a formal framework for the specification of complex reasoning. MC systems provide the ability to structure the specification of “global” reasoning in terms of “local” reasoning subpatterns. Each subpattern is modeled as a deduction in a context, formally defined as an axiomatic formal system. the global reasoning pattern is modeled as a concatenation of contextual deductions via bridge rules, i.e., inference rules that infer a fact in one context from facts asserted in other contexts. Besides the formal framework, in this article we propose a three-layer architecture designed to specify and automatize complex reasoning. At the first level we have object-level contexts (called s-contexts) for domain specifications. Problem-solving principles and, more in general, meta-level knowledge about the application domain is specified in a distinct context, called Problem-Solving Context (PSC). On top of s-contexts and PSC, we have a further context, called MT, where it is possible to specify strategies to control multicontext reasoning spanning through s-contexts and PSC. We show how GETFOL can be used as a computer tool for the implementation of MC systems and for the automatization of multicontext deductions. © 1995 John Wiley & Sons, Inc.     

Designing conversational agents: effect of conversational form on our comprehension

We have developed a broadcasting agent system, public opinion channel (POC) caster, which generates understandable conversational form from text-based documents. The POC caster circulates the opinions of community members by using conversational form in a broadcasting system on the Internet. We evaluated its transformation rules in two experiments. In experiment 1, we examined our transformation rules for conversational form in relation to sentence length. Twenty-four participants listened to two types of sentence (long sentences and short sentences) with conversational form or with single speech. In experiment 2, we investigated the relationship between conversational form and the user’s knowledge level. Forty-two participants (21 with a high knowledge level and 21 with a low knowledge level) were selected for a knowledge task and listened to two kinds of sentence (sentences about a well-known topic or sentences about an unfamiliar topic). Our results indicate that the conversational form aided comprehension, especially for long sentences and when users had little knowledge about the topic. We explore possible explanations and implications of these results with regard to human cognition and text comprehension.     

面向复杂产品的多层智能推理框架

案例推理是基于知识的问题求解方法.克服复杂产品进行单层案例推理所面临的粗粒度问题,考虑到复杂产品案例表达多层次的特点,根据领域知识和部件功能相似性,基于XML描述的部件层次互换约束规则,实现深层次的案例细节调整和修改,从而建立了多层智能推理方法框架,为多层次复杂产品案例推理问题提供可行的解决方案.并以某类复杂产品采办全生命周期中的概念设计为例说明多层智能推理框架问题求解的有效性和面向用户服务的支持效率.     

Ontology-based context modeling for emotion recognition in an intelligent web

We describe an ontological model for representation and integration of electroencephalographic (EEG) data and apply it to detect human emotional states. The model (BIO_EMOTION) is an ontology-based context model for emotion recognition and acts as a basis for: (1) the modeling of users’ contexts, including user profiles, EEG data, the situation and environment factors, and (2) supporting reasoning on the users’ emotional states. Because certain ontological concepts in the EEG domain are ill-defined, we formally represent and store these concepts, their taxonomies and high-level representation (i.e., rules) in the model. To evaluate the effectiveness for inferring emotional states, DEAP dataset is used for model reasoning. Result shows that our model reaches an average recognition ratio of 75.19 % on Valence and 81.74 % on Arousal for eight participants. As mentioned above, the BIO-EMOTION model acts like a bridge between users’ emotional states and low-level bio-signal features. It can be integrated in user modeling techniques, and be used to model web users’ emotional states in human-centric web aiming to provide active, transparent, safe and reliable services to users. This work aims at, in other words, creating an ontology-based context model for emotion recognition using EEG. Particularly, this model completely implements the loop body of the W2T data cycle once: from low-level EEG feature acquisition to emotion recognition. A long-term goal for the study is to complete this model to implement the whole W2T data cycle.     

A structural equation modeling approach to generate explanations for induced rules

The performance of an expert system depends on the quality and validity of the domain-specific knowledge built into the system. In most cases, however, domain knowledge (e.g. stock market behavior knowledge) is unstructured and differs from one domain expert to another. So, in order to acquire domain knowledge, expert system developers often take an induction approach in which a set of general rules is constructed from past examples. Expert systems based upon the induced rules were reported to perform quite well in the hold-out sample test.

However, these systems hardly provide users with an explanation which would clarify the results of a reasoning process. For this reason, users would remain unsure about whether to accept the system conclusion or not. This paper presents an approach in which explanations about the induced rules are constructed. Our approach applies the structural equation model to the quantitative data, the qualitative format of which was originally used in rule induction. This approach was implemented with Korean stock market data to show that a plausible explanation about the induced rule can be constructed.     

A Computational Framework for Practical Social Reasoning

This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called the expectation strategy behavior (ESB) framework, is based on (i) using sets of update rules for social beliefs tied to observations (so‐called expectations), (ii) bounding the amount of reasoning to be performed over these rules by defining a reasoning strategy, and (iii) influencing the agent's decision‐making logic by means of behaviors conditioned on the truth status of current and future social beliefs. We introduce the foundations of ESB conceptually and present a formal framework and an actual implementation of a reasoning engine, which is specifically combined with a general (belief–desire–intention‐based) practical reasoning programming system. We illustrate the generality of ESB through select case studies, which show that it is able to represent and implement different typical styles of social reasoning. The broad coverage of existing social reasoning methods, the modularity that derives from its declarative nature, and its focus on practical implementation make ESB a useful tool for building advanced socially reasoning agents.     

Dealing with explicit preferences and uncertainty in answer set programming

In this paper, we show how the formalism of Logic Programs with Ordered Disjunction (LPODs) and Possibilistic Answer Set Programming (PASP) can be merged into the single framework of Logic Programs with Possibilistic Ordered Disjunction (LPPODs). The LPPODs framework embeds in a unified way several aspects of common-sense reasoning, nonmonotonocity, preferences, and uncertainty, where each part is underpinned by a well established formalism. On one hand, from LPODs it inherits the distinctive feature of expressing context-dependent qualitative preferences among different alternatives (modeled as the atoms of a logic program). On the other hand, PASP allows for qualitative certainty statements about the rules themselves (modeled as necessity values according to possibilistic logic) to be captured. In this way, the LPPODs framework supports a reasoning which is nonmonotonic, preference- and uncertainty-aware. The LPPODs syntax allows for the specification of (1) preferences among the exceptions to default rules, and (2)?necessity values about the certainty of program rules. As a result, preferences and uncertainty can be used to select the preferred uncertain default rules of an LPPOD and, consequently, to order its possibilistic answer sets. Furthermore, we describe the implementation of an ASP-based solver able to compute the LPPODs semantics.     

The power of a nod and a glance: Envelope vs. emotional feedback in animated conversational agents

In this article we describe results froman experiment of user interaction with autonomous , human - like ( humanoid ) conversational agents . We hypothesize that for embodied conversational agents , nonverbal behaviors related to the process of conversation , what we call envelope feedback, is much more important than other feedback , such as emotional expression . We test this hypothesis by having subjects interact with three autonomous agents , all capable of full - duplex multimodal interaction: able to generate and recognize speech , intonation , facial displays , and gesture . Each agent , however , gave a different kind of feedback: ( 1 ) content - related only , ( 2 ) content + envelope feedback , and ( 3 ) content + emotional . Content-related feedback includes answering questions and executing commands; envelope feedback includes behaviors such as gaze , manual beat gesture , and head movements; emotional feedback includes smiles and looks of puzzlement . Subjects' evaluations of the systemwere collected with a questionnaire , and videotapes of their speech patterns and behaviors were scored according to how often the users repeated themselves , how often they hesitated , and how often they got frustrated . The results confirmour hypothesis that envelope feedback is more important in interaction than emotional feedback and that envelope feedback plays a crucial role in supporting the process of dialog . A secondary result fromthis study shows that users give our multimodal conversational humanoids very high ratings of lifelikeness and fluidity of interaction when the agents are capable of giving such feedback .     

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks.     

Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions

Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably.     

对话推荐算法研究综述

推荐系统是一种通过理解用户的兴趣和偏好帮助用户过滤大量无效信息并获取感兴趣的信息或者物品的信息过滤系统.目前主流的推荐系统主要基于离线的、历史的用户数据,不断训练和优化线下模型,继而为在线的用户推荐物品,这类训练方式主要存在3个问题:基于稀疏且具有噪声的历史数据估计用户偏好的不可靠估计、对影响用户行为的在线上下文环境因素的忽略和默认用户清楚自身偏好的不可靠假设.由于对话系统关注于用户的实时反馈数据,获取用户当前交互的意图,因此“对话推荐”通过结合对话形式与推荐任务成为解决传统推荐问题的有效手段.对话推荐将对话系统实时交互的数据获取方式应用到推荐系统中,采用了与传统推荐系统不同的推荐思路,通过利用在线交互信息,引导和捕捉用户当前的偏好兴趣,并及时进行反馈和更新.在过去的几年里,越来越多的研究者开始关注对话推荐系统,这一方面归功于自然语言处理领域中语音助手以及聊天机器人技术的广泛使用,另一方面受益于强化学习、知识图谱等技术在推荐策略中的成熟应用.将对话推荐系统的整体框架进行梳理,将对话推荐算法研究所使用的数据集进行分类,同时对评价对话推荐效果的相关指标进行讨论,重点关注于对话推荐系统中的后...     

A fuzzy expert system for a service centre of spare parts

This paper presents the design of an approximate reasoning framework for an expert system prototype for a service centre of spare parts, to which customers bring failed items for repair. The design development is fundamentally based upon an analysis of a queuing model associated with the service centre system problem. This queuing model provides a prerequisite insight and the knowledge about such a service system. The building process for the framework is described in a case study utilizing the queuing model, namely, M/M/c repair systems with spares. The objective here is to aid management in determining certain decision policies and the capacities which are critical to them. Within the approximate reasoning framework, the identification and the construction of the basic rules that contain uncertain (vague, ambiguous, fuzzy) linguistic terms are described, as well as the specification of the membership functions that represent the meaning of such linguistic terms. Consistency of rules is studied in accordance with the internal relationships between system variables. Approximate Analogical Reasoning with a tree search is used as the inference engine of the expert system. Approximate reasoning results are compared with analytical results.     

Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents

Building a collaborative trusting relationship with users is crucial in a wide range of applications, such as advice-giving or financial transactions, and some minimal degree of cooperativeness is required in all applications to even initiate and maintain an interaction with a user. Despite the importance of this aspect of human–human relationships, few intelligent systems have tried to build user models of trust, credibility, or other similar interpersonal variables, or to influence these variables during interaction with users. Humans use a variety of kinds of social language, including small talk, to establish collaborative trusting interpersonal relationships. We argue that such strategies can also be used by intelligent agents, and that embodied conversational agents are ideally suited for this task given the myriad multimodal cues available to them for managing conversation. In this article we describe a model of the relationship between social language and interpersonal relationships, a new kind of discourse planner that is capable of generating social language to achieve interpersonal goals, and an actual implementation in an embodied conversational agent. We discuss an evaluation of our system in which the use of social language was demonstrated to have a significant effect on users’ perceptions of the agent’s knowledgableness and ability to engage users, and on their trust, credibility, and how well they felt the system knew them, for users manifesting particular personality traits.This revised version was published online in July 2005 with corrections to the author name Bickmore.     

An approach to fuzzy default reasoning for function approximation

This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap with each other in the input space. General rules work as default rules in our fuzzy rule base. First, we briefly describe existing approaches to the handling of default rules in the framework of possibility theory. Next, we show that standard interpolation-based fuzzy reasoning leads to counterintuitive results when general rules include specific rules with different consequents. Then, we demonstrate that intuitively acceptable results are obtained from a non-standard inclusion-based fuzzy reasoning method. Our approach is based on the preference for more specific rules, which is a commonly used idea in the field of default reasoning. When a general rule includes a specific rule and they are both compatible with an input vector, the weight of the general rule is discounted in fuzzy reasoning. We also discuss the case where general rules do not perfectly but partially include specific rules. Then we propose a genetics-based machine learning (GBML) algorithm for extracting a small number of fuzzy if-then rules with different specificity levels from numerical data using our inclusion-based fuzzy reasoning method. Finally, we describe how our approach can be applied to the approximate realization of fuzzy number-valued nonlinear functions