Dynamic Partition of Collaborative Multiagent Based on Coordination Trees

In team Markov games research, it is difficult for an individual agent to calculate the reward of collaborative agents dynamically. We present a coordination tree structure whose nodes are agent subsets or an agent. Two kinds of weights of a tree are defined which describe the cost of an agent collaborating with an agent subset. We can calculate a collaborative agent subset and its minimal cost for collaboration using these coordination trees. Some experiments of a Markov game have been done by using this novel algorithm. The results of the experiments prove that this method outperforms related multi-agent reinforcement-learning methods based on alterable collaborative teams

Modeling Role-Based Agent Team 1

Abstract. The problem of ensuring agents work as an effective team in dynamic distributed environments still remains a challenging issue. In this paper we proposed a role-based team model. In our model, the role characterizes the responsibilities and provides logic patterns to achieve certain goals and cooperate with others. The agent is an autonomous execution unit and follows the logic patterns that the role provides. We also developed algorithms and mechanisms to evolve the plan of a role to the plan of an agent. Our role-based team model allows the split of roles (who define the plans) and agents (who execute the plans) in team plans, and dynamic role-agent assignment. It also achieves a certain level of plan reusability. We present two experiments which show plan reusability and its flexibility in supporting simultaneously plan invocation. Keywords: Agent teamwork, Role, Plan.

Generating Artificial Corpora for Plan Recognition

Corpora for training plan recognizers are scarce and di#- cult to gather from humans. However, corpora could be a boon to plan recognition research, providing a platform to train and test individual recognizers, as well as allow di#erent recognizers to be compared. We present a novel method for generating artificial corpora for plan recognition

Sto(ry)chastics: a Bayesian Network Architecture for User Modeling and Computational Storytelling for Interactive Spaces

This paper presents sto(ry)chastics, a user-centered approach for computational storytelling for real-time sensor-driven multimedia audiovisual stories, such as those that are triggered by the body in motion in a sensor-instrumented interactive narrative space. With sto(ry)chastics the coarse and noisy sensor inputs are coupled to digital media outputs via a user model, which is estimated probabilistically by a Bayesian network. To illustrate sto(ry)chastics, this paper describes the museum wearable, a device which delivers an audiovisual narration interactively in time and space to the visitor as a function of the estimated visitor type. The wearable relies on a custom-designed long-range infrared locationidentification sensor to gather information on where and how long the visitor stops in the museum galleries and uses this information as input to, or observations of, a (dynamic) Bayesian network. The network has been tested and validated on observed visitor tracking data by parameter learning using the Expectation Maximization (EM) algorithm, and by performance analysis of the model with the learned parameters

Reinforcement learning for adaptive theory of mind in the sigma cognitive architecture

One of the most common applications of human intelligence is social interaction, where people must make effective decisions despite uncertainty about the potential behavior of others around them. Reinforcement learning (RL) provides one method for agents to acquire knowledge about such interactions. We investigate different methods of multiagent reinforcement learning within the Sigma cognitive architecture. We leverage Sigma’s architectural mechanism for gradient descent to realize four different approaches to multiagent learning: (1) with no explicit model of the other agent, (2) with a model of the other agent as following an unknown stationary policy, (3) with prior knowledge of the other agent’s possible reward functions, and (4) through inverse reinforcement learning (IRL) of the other agent’s reward function. While the first three variations re-create existing approaches from the literature, the fourth represents a novel combination of RL and IRL for social decision-making. We show how all four styles of adaptive Theory of Mind are realized through Sigma’s same gradient descent algorithm, and we illustrate their behavior within an abstract negotiation task