Multiagent Teamwork: Hybrid Approaches

Conference paper published in CSI Communications</p

Hybrid POMDP-BDI: An Agent Architecture with Online Stochastic Planning and Desires with Changing Intensity Levels

Partially observable Markov decision processes (POMDPs) and the belief-desire-intention (BDI) framework have several complimentary strengths. We propose an agent architecture which combines these two powerful approaches to capitalize on their strengths. Our architecture introduces the notion of intensity of the desire for a goal’s achievement. We also define an update rule for goals’ desire levels. When to select a new goal to focus on is also defined. To verify that the proposed architecture works, experiments were run with an agent based on the architecture, in a domain where multiple goals must continually be achieved. The results show that (i) while the agent is pursuing goals, it can concurrently perform rewarding actions not directly related to its goals, (ii) the trade-off between goals and preferences can be set effectively and (iii) goals and preferences can be satisfied even while dealing with stochastic actions and perceptions. We believe that the proposed architecture furthers the theory of high-level autonomous agent reasoning

A hybrid POMDP-BDI agent architecture with online stochastic planning and plan caching

This article presents an agent architecture for controlling an autonomous agent in stochastic, noisy environments. The architecture combines the partially observable Markov decision process (POMDP) model with the belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent architecture takes the best features from the two approaches, that is, the online generation of reward-maximizing courses of action from POMDP theory, and sophisticated multiple goal management from BDI theory. We introduce the advances made since the introduction of the basic architecture, including (i) the ability to pursue and manage multiple goals simultaneously and (ii) a plan library for storing pre-written plans and for storing recently generated plans for future reuse. A version of the architecture is implemented and is evaluated in a simulated environment. The results of the experiments show that the improved hybrid architecture outperforms the standard POMDP architecture and the previous basic hybrid architecture for both processing speed and effectiveness of the agent in reaching its goals

Designing a BDI agent reactant model of behavioural change intervention

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state. The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions.Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioral change interventions. Nevertheless, existing BDI agent models do not represent agent’s behavioral factors explicitly. This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioral change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioral change and psychological theories.Also, using mathematical analysis the model was evaluated. The objective of the proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions.The model will be realized in an agent based application that motivates children towards oral hygiene. The study explicitly depicts how agent’s behavioral factors interact to enhance behavior change which will assist agent-based intervention designers to be able to design intervention that will be void of reactance

Designing a BDI agent model for behavioural change process

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state.The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions. Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioural change interventions.Nevertheless, existing BDI agent models do not represent agent’s behavioural factors explicitly.This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioural change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioural change and psychological theories.The objective of this proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions.The model will be realized in an agent-based application that motivates children towards oral hygiene

Formal Verification of Autonomous Vehicle Platooning

The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the new autonomous behaviors of the vehicles in these platoons must be certified. An appropriate representation for vehicle platooning is as a multi-agent system in which each agent captures the "autonomous decisions" carried out by each vehicle. In order to ensure that these autonomous decision-making agents in vehicle platoons never violate safety requirements, we use formal verification. However, as the formal verification technique used to verify the agent code does not scale to the full system and as the global verification technique does not capture the essential verification of autonomous behavior, we use a combination of the two approaches. This mixed strategy allows us to verify safety requirements not only of a model of the system, but of the actual agent code used to program the autonomous vehicles

The power of teams that disagree:team formation in large action spaces

Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were never asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity, where we prove that the performance of a diverse team improves as the size of the action space increases. Moreover, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that give further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where a diverse team improves in performance as the board size increases, and eventually overcomes a uniform team