Computational conflicts: conflict modeling for distributed intelligent systems
Computational conflicts: conflict modeling for distributed intelligent systems
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Constructing Robust Conversation Policies in Dynamic Agent Communities
Issues in Agent Communication
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
An Analysis of Online Customer Complaints: Implications for Web Complaint Management
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Argumentation-based negotiation
The Knowledge Engineering Review
A Defeasible Logic Programming System for the Web
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Visual and Spatial Analysis
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Journal of Intelligent Information Systems
Reasoning about attitudes of complaining customers
Knowledge-Based Systems
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining the text information to optimizing the customer relationship management
Expert Systems with Applications: An International Journal
Identifying terrorist activity with AI plan recognition technology
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A Mathematical Model of Dialog
Electronic Notes in Theoretical Computer Science (ENTCS)
Learning in BDI multi-agent systems
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
Analyzing conflicts with concept-based learning
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
Predicting customer churn through interpersonal influence
Knowledge-Based Systems
Robotic clusters: Multi-robot systems as computer clusters
Robotics and Autonomous Systems
Exhaustive simulation of consecutive mental states of human agents
Knowledge-Based Systems
Hi-index | 0.00 |
We explore the common patterns of human behavior, expressed via communicative actions, and displayed in various domains of human activities associated with conflicts. We build the generic methodology based on machine learning and reasoning to predict specific communicative actions of human agents, given previous sequence of communicative actions of themselves and their opponents. This methodology is applied to textual as well as structured data on inter-human conflicts of diverse modalities. Scenarios are represented by directed graphs with labeled vertices (for communicative actions) and arcs (for temporal and causal relationships between subjects of these actions). Scenario representation and learning techniques are firstly developed in the domain of textual customer complaints, and then applied to such problems as predicting an outcome of international conflicts, assessment of an attitude of a security clearance candidate, mining emails for suspicious emotional profiles, and recognizing suspicious behavior of cell phone users. We present an evaluation of the proposed methodology in the domain of customer complaint and conduct some comparative evaluation in the other domains mentioned above. Successful use of the proposed methodology in rather distinct domains shows its adequacy for mining human attitude-related data in a wide range of applications.