The purpose of the paper is to introduce a new approach of planning called Assumption-Based Planning. This approach is a very interesting way to devise a planner based on a multi-agent system in which the production of a global shared plan is obtained by conjecture/refutation cycles. Contrary to classical approaches, our contribution relies on the agents reasoning that leads to the production o...

Planning for partially observable, nondeterministic domains is a very significant and computationally hard problem. Often, reasonable assumptions can be drawn over expected/nominal dynamics of the domain; using them to constrain the search may lead to dramatically improve the efficiency in plan generation. In turn, the execution of assumption-based plans must be monitored to prevent runtime fai...

The purpose of this poster is to introduce a dialectical theory for plan synthesis based on a multiagent approach. This approach is a promising way to devise systems able to take into account partial knowledge and heterogeneous skills of agents. We propose to consider the planning problem as a defeasible reasoning where agents exchange proposals and counter-proposals and are able to conjecture ...

A popular technique for getting to a goal location in unknown terrain is planning with the freespace assumption. The robot assumes that the terrain is clear unless it knows otherwise. It always plans a shortest path to the goal location and re-plans whenever it detects an obstacle that blocks its path or, more generally, when it detects that its current path is no longer optimal. It has been un...

Assumption-based planning (ABP) is a recently proposed alternative to conformant planning. Like conformant planning, it is designed for domains in which the initial state is uncertain, and in which actions are deterministic. Unlike conformant planning, an ABP plan may make commonsensical assumptions about the initial state, which may result in simpler, easier-to-communicate plans that achieve t...