Social Continual Planning in Open Multiagent Systems: a First Study

Abstract. We describe a Multiagent Planning approach, named Social Continual Planning, that tackles open scenarios, where agents can join and leave the system dynamically. The planning task is not defined from a global point of view, setting a global objective, but we allow each agent to pursue its own subset of goals. We take a social perspective where, although each agent has its own planning task and planning algorithm, it needs to get engaged with others for accomplishing its own goals. Cooperation is not forced but, thanks to the abstraction of social commitment, stems from the needs of the agents

Home alone: autonomous extension and correction of spatial representations

In this paper we present an account of the problems faced by a mobile robot given an incomplete tour of an unknown environment, and introduce a collection of techniques which can generate successful behaviour even in the presence of such problems. Underlying our approach is the principle that an autonomous system must be motivated to act to gather new knowledge, and to validate and correct existing knowledge. This principle is embodied in Dora, a mobile robot which features the aforementioned techniques: shared representations, non-monotonic reasoning, and goal generation and management. To demonstrate how well this collection of techniques work in real-world situations we present a comprehensive analysis of the Dora system’s performance over multiple tours in an indoor environment. In this analysis Dora successfully completed 18 of 21 attempted runs, with all but 3 of these successes requiring one or more of the integrated techniques to recover from problems

A new approach for continual planning

International audienceDevising intelligent robots or agents that interact with humans is a major challenge for artificial intelligence. In such contexts, agents must constantly adapt their decisions according to human activities and modify their goal. In this extended abstract, we present a novel continual planning approach, called Moving Goal Planning (MGP) to adapt plans to goal evolutions. This approach draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible the start of new searches when the goal changes over time. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing actions of the current plan brings MGP closer to the new goal

Evolution of a supply chain management game for the trading agent competition

TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt