Some strategies for the simulation of vocabulary agreement in multi-agent communities

In this paper, we present several experiments of belief propagation in multi-agent communities. Each agent in the simulation has an initial random vocabulary (4 words) corresponding to each possible movement (north, south, east and west). Agents move and communicate the associated word to the surrounding agents, which can be convinced by the 'speaking agent', and change their corresponding word by 'imitation'. Vocabulary uniformity is achieved, but strong interactions and competition can occur between dominant words. Several moving and trusting strategies as well as agent roles are analyzed.This paper has been sponsored by the Spanish Interdepartmental Commission of Science and Technology (CICYT), project number TEL1999-0181

Strategies for fast convergence in semiotic dynamics

Semiotic dynamics is a novel field that studies how semiotic conventions spread and stabilize in a population of agents. This is a central issue both for theoretical and technological reasons since large system made up of communicating agents, like web communities or artificial embodied agents teams, are getting widespread. In this paper we discuss a recently introduced simple multi-agent model which is able to account for the emergence of a shared vocabulary in a population of agents. In particular we introduce a new deterministic agents' playing strategy that strongly improves the performance of the game in terms of faster convergence and reduced cognitive effort for the agents.Comment: 6 pages, 6 figure

Consequence of reputation in an open-ended Naming Game

We study a modified version of the Naming Game, a recently introduced model which describes how shared vocabulary can emerge spontaneously in a population without any central control. In particular, we introduce a new mechanism that allows a continuous interchange with the external inventory of words. A novel playing strategy, influenced by the hierarchical structure that individuals' reputation defines in the community, is implemented. We analyze how these features influence the convergence times, the cognitive efforts of the agents and the scaling behavior in memory and time.Comment: 6 pages, 6 figure

Experimenting with the Gaze of a Conversational Agent

We have carried out a pilot experiment to investigate the effects of different eye gaze behaviors of a cartoon-like talking face on the quality of human-agent dialogues. We compared a version of the talking face that roughly implements some patterns of humanlike behavior with two other versions. We called this the optimal version. In one of the other versions the shifts in gaze were kept minimal and in the other version the shifts would occur randomly. The talking face has a number of restrictions. There is no speech recognition, so questions and replies have to\ud be typed in by the users of the systems. Despite this restriction we found that participants that conversed with the optimal agent appreciated the agent more than participants that conversed with the other agents. Conversations with the optimal version proceeded more efficiently. Participants needed less time to complete their task

Controlling the Gaze of Conversational Agents

We report on a pilot experiment that investigated the effects of different eye gaze behaviours of a cartoon-like talking face on the quality of human-agent dialogues. We compared a version of the talking face that roughly implements some patterns of human-like behaviour with\ud two other versions. In one of the other versions the shifts in gaze were kept minimal and in the other version the shifts would occur randomly. The talking face has a number of restrictions. There is no speech recognition, so questions and replies have to be typed in by the users\ud of the systems. Despite this restriction we found that participants that conversed with the agent that behaved according to the human-like patterns appreciated the agent better than participants that conversed with the other agents. Conversations with the optimal version also\ud proceeded more efficiently. Participants needed less time to complete their task

Debbie, the Debate Bot of the Future

Chatbots are a rapidly expanding application of dialogue systems with companies switching to bot services for customer support, and new applications for users interested in casual conversation. One style of casual conversation is argument, many people love nothing more than a good argument. Moreover, there are a number of existing corpora of argumentative dialogues, annotated for agreement and disagreement, stance, sarcasm and argument quality. This paper introduces Debbie, a novel arguing bot, that selects arguments from conversational corpora, and aims to use them appropriately in context. We present an initial working prototype of Debbie, with some preliminary evaluation and describe future work.Comment: IWSDS 201

I Don't Want to Think About it Now:Decision Theory With Costly Computation

Computation plays a major role in decision making. Even if an agent is willing to ascribe a probability to all states and a utility to all outcomes, and maximize expected utility, doing so might present serious computational problems. Moreover, computing the outcome of a given act might be difficult. In a companion paper we develop a framework for game theory with costly computation, where the objects of choice are Turing machines. Here we apply that framework to decision theory. We show how well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on), belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions), and the status quo bias (people are much more likely to stick with what they already have) can be easily captured in that framework. Finally, we use the framework to define some new notions: value of computational information (a computational variant of value of information) and and computational value of conversation.Comment: In Conference on Knowledge Representation and Reasoning (KR '10