Kobti / Wu Advances in Artificial Intelligence

20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007, Proceedings
2007
ISBN: 978-3-540-72665-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 565 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

This book constitutes the refereed proceedings of the 20th Australian Joint Conference on Artificial Intelligence, AI 2007, held in Gold Coast, Australia, in December 2007.

The 58 revised full papers and 40 revised short papers presented together with the extended abstracts of three invited speeches were carefully reviewed and selected from 194 submissions.

The papers are organized in topical sections on machine learning, neural networks, evolutionary computing, constraint satisfaction, satisfiability, automated reasoning, knowledge discovery, robotics, social intelligence, ontologies and semantic Web, natural language systems, knowledge representation, expert systems, applications of AI, and short papers.

Written for: Researchers and professionals

Keywords: AI, QoS, Web intelligence, agent technology, artificial intelligence, classification, clustering, cognitive technologies, computational intelligence, computer vision, constraint satisfaction, data mining, decision making, evolutionary computing, fuzzy theory and algorithms, game theory, genetic algorithms, image processing, information extraction, intelligent agents, intelligent infomration systems, knowledge processing, machine learning, multi-agent systems, natural language processing, neural networks, ontology, optimization, pattern recognition, probabilistic methods, reasoning, robotics, semantic Web.

Zielgruppe

Research

Autoren/Hrsg.

Kobti, Ziad

Wu, Dan

Inhaltsverzeichnis

Session 1. Agents.- Modeling Role-Based Agent Team.- Distributed Collaborative Filtering for Robust Recommendations Against Shilling Attacks.- Competition and Coordination in Stochastic Games.- Multiagent-Based Dynamic Deployment Planning in RTLS-Enabled Automotive Shipment Yard.- R-FRTDP: A Real-Time DP Algorithm with Tight Bounds for a Stochastic Resource Allocation Problem.- A Reorganization Strategy to Build Fault-Tolerant Multi-Agent Systems.- Session 2. Bioinformatics.- A Multi-site Subcellular Localizer for Fungal Proteins.- Selecting Genotyping Oligo Probes Via Logical Analysis of Data.- Session 3. Classification.- Learning the Semantic Meaning of a Concept from the Web.- On Combining Dissimilarity-Based Classifiers to Solve the Small Sample Size Problem for Appearance-Based Face Recognition.- A Novel Approach for Automatic Palmprint Recognition.- ICS: An Interactive Classification System.- Fast Most Similar Neighbor Classifier for Mixed Data.- Performance Measures in Classification of Human Communications.- Cost-Sensitive Decision Trees with Pre-pruning.- Probability Based Metrics for Locally Weighted Naive Bayes.- Recurrent Boosting for Classification of Natural and Synthetic Time-Series Data.- Pattern Classification in No-Limit Poker: A Head-Start Evolutionary Approach.- Session 4. Constraint Satisfaction.- Managing Conditional and Composite CSPs.- Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks.- Session 5. Data Mining.- A Clustering Algorithm Based on Adaptive Subcluster Merging.- Efficient Algorithms for Video Association Mining.- Distributed Data Mining in a Ubiquitous Healthcare Framework.- Constructing a User Preference Ontology for Anti-spam Mail Systems.- Question Answering Summarization of Multiple Biomedical Documents.- A Profit-Based Business Model for Evaluating Rule Interestingness.- Session 6. Knowledge Representation and Reasoning.- Reasoning About Operations on Sets.- Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation.- Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks.- Adding Local Constraints to Bayesian Networks.- On the Use of Possibilistic Bases for Local Computations in Product-Based Possibilistic Networks.- Reasoning with Conditional Preferences Across Attributes.- Path Propagation for Inference in Bayesian Networks.- Problem-Solving Knowledge Mining from Users’ Actions in an Intelligent Tutoring System.- Incremental Neighborhood Graphs Construction for Multidimensional Databases Indexing.- Session 7. Learning.- Learning Network Topology from Simple Sensor Data.- Reinforcement Learning in Nonstationary Environment Navigation Tasks.- On the Stability and Bias-Variance Analysis of Kernel Matrix Learning.- Session 8. Natural Language.- Query-Based Summarization of Customer Reviews.- Multi-state Directed Acyclic Graphs.- Fuzzy Clustering for Topic Analysis and Summarization of Document Collections.- Creating a Fuzzy Believer to Model Human Newspaper Readers.- Rethinking the Semantics of Complex Nominals.- A Hybrid Approach to Improving Automatic Speech Recognition Via NLP.- Session 9. Planning.- Planning in Multiagent Expedition with Collaborative Design Networks.- Hierarchical Shortest Pathfinding Applied to Route-Planning for Wheelchair Users.

Leseproben

Modeling Role-Based Agent Team* (p. 12)
1 Introduction
Teamwork is becoming increasingly important in many dynamic multi-agent systems [13]. Agents in a team need to form joint mental states which drive agents to act together as a team and form the interactions leading their individual actions to team efforts [5, 7]. To simulate teamwork, a teamwork language is demanded to explicitly express the mental states underlying teamwork. In our opinion, the effective design of a teamwork language requires two aspects to consider. First, it should be able to handle unexpected uncertainties occurred in complex and dynamic domains, such as dynamic changes in team’s goals, team members’ unexpected failures to fulfill their responsibilities, decision-making in dynamic environment, and dynamically backing up other team members.

Second, considering the perspective of software engineering, the teamwork language would better allow specify teamwork knowledge conceptually for being reused, particularly, team plans are better specified in terms of abstract entities, instead of specific agents, so as to be reused by different teams of agents. A lower level of abstraction, role, is currently used by many researcher of multiagent systems to close this gap [14, 9, 11, 6, 16]. Biddle and Thomas’s role theory views role as the concept of partitioning behaviors and emphasizing coordination and cooperation [2].

Becht’s ROPE (Role Oriented Programming Environment for multiagent systems) uses roles to decouple the organization of agents from the structure of cooperation processes [1]. Cooperation process is designed from a global perspective and largely independent of concrete agents so that shifting cooperative behavior does not require changing agents (agents can fill multiple roles and switch between them). Stone and Veloso introduced roles as a mechanism for specifying an agent’s internal and external behaviors and decomposing team tasks [12], they then used this to model robot soccer. A formation decomposes the task space by defining a set of roles. There are as many roles as there are agents in the team, so that each role is filled by one agent. The mapping between agents and role is not pre-specified.

In this paper, we propose a role-based teamwork language. Different with the existing work, we use roles and role variables distinguish static (by roles) and dynamic (by role variables) action associations, and when delegating roles and role variables in a plan to agents, we have the agents form s joint mental state to enforce the execution of the plan as a team effort, particularly the sub-actions in the plan will be executed coherently. Our concepts of role and role variable enable our mechanisms of task decomposition and delegation, by which role-based plans drive agents to actually execute teamwork. Our mechanism of task decomposition is based on a notion of responsibility, which is defined in terms of what a responsibility contains and how a responsibility impacts the mental states of the agent(s) taking the responsibility. Our mechanism of task delegation has three steps:

1) a team task is translated to a team responsibility which is represented by a graph,

2) through decomposing the team responsibility graph to individual responsibility graphs, a team task is decomposed to individual sub-tasks, and

3) individual sub-tasks are delegated to agents.