Best Technical Paper.- On the Classification Performance of TAN and General Bayesian Networks.- CBR and Classification.- Code Tagging and Similarity-based Retrieval with myCBR.- Sparse Representations for Pattern Classification using Learned Dictionaries.- Qualitative Hidden Markov Models for Classifying Gene Expression Data.- Description Identification and the Consistency Problem.- AI Techniques.- Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs.- Restart-Based Genetic Algorithm for the Quadratic Assignment Problem.- Constraint Satisfaction and Fixes: Revisiting Sisyphus VT.- On a Control Parameter Free Optimization Algorithm.- Argumentation And Negotiation.- PISA - Pooling Information from Several Agents: Multiplayer Argumentation from Experience.- Agent-Based Negotiation in Uncertain Environments.- Automated Bilateral Negotiation and Bargaining Impasse.- Intelligent Systems.- Exploring Design Space For An Integrated Intelligent System.- A User-Extensible and Adaptable Parser Architecture.- The Reactive-Causal Architecture: Introducing an Emotion Model along with Theories of Needs.- Automation of the Solution of Kakuro Puzzles.- From Machine Learning to E-Learning.- The Bayesian Learning Automaton - Empirical Evaluation with Two-Armed Bernoulli Bandit Problems.- Discovering Implicit Intention-Level Knowledge from Natural-Language Texts.- EMADS: An Extendible Multi-Agent Data Miner.- Designing a Feedback Component of an Intelligent Tutoring System for Foreign Language.- Decision Making.- An Algorithm for Anticipating Future Decision Trees from Concept-Drifting Data.- Polarity Assignment to Causal Information Extracted from Financial Articles Concerning Business Performance of Companies.- Redux exp : An Open-source Justification-basedExplanation Support Server.- Short Papers.- Immunity-based hybrid evolutionary algorithm for multi-objective optimization.- Parallel Induction of Modular Classification Rules.- Transform Ranking: a New Method of Fitness Scaling in Genetic Algorithms.- Architecture of Knowledge-based Function Approximator.- Applying Planning Algorithms to Argue in Cooperative Work.- Universum Inference and Corpus Homogeneity.
"ARGUMENTATION AND NEGOTIATION PISA - Pooling Information from Several Agents: Multiplayer Argumentation from Experience (p. 133-134)
Maya Wardeh, Trevor Bench-Capon and Frans Coenen
Abstract In this paper a framework, PISA (Pooling Information from Several Agents), to facilitate multiplayer (three or more protagonists), “argumentation from experience” is described. Multiplayer argumentation is a form of dialogue game involving three or more players. The PISA framework is founded on a two player argumentation framework, PADUA (Protocol for Argumentation Dialogue Using Association Rules), also developed by the authors.
One of the main advantages of both PISA and PADUA is that they avoid the resource intensive need to predefine a knowledge base, instead data mining techniques are used to facilitate the provision of “just in time” information. Many of the issues associated with multiplayer dialogue games do not present a significant challenge in the two player game. The main original contributions of this paper are the mechanisms whereby the PISA framework addresses these challenges.
1 Introduction
In many situations agents need to pool their information in order to solve a problem. For example in the field of classification one agent may have a rule that will give the classification, but that agent may be unaware of the facts which will enable the rule to be applied, whereas some other agent does know these facts. Individually neither can solve the problem, but together they can. One method to facilitate information sharing is to enable a dialogue between the two agents.
Often this dialogue takes the form of a persuasion dialogue where two agents act as advocates for alternative points of view. A survey of such approaches is given in (Prakken 2006). The systems discussed by Prakken suppose that agent knowledge is represented in the form of belief bases, essentially a set of rules and facts. In consequence dialogue moves are strongly related to knowledge represented in this form. A typical set of moves for the systems in (Prakken 2006) are:
- Claim P: P is the head of some rule
- Why P: Seeks the body of rule for which P is head
- Concede P: agrees that P is true
- Retract P: denies that P is true "
