Distributed abductive reasoning with constraints

Abductive reasoning is a powerful inference mechanism that can generate conditional proofs. The combination of Abduction and Logic Programming (ALP) [5] has many known applications, such as planning, scheduling, cognitive robotics, medical diagnosis and policy analysis [3]. However, most abductive frameworks [7, 4, 8] rely on a centrally executed proof procedure whereas many of the application problems are distributed by nature. Confidentiality and communication overhead concerns often preclude transmitting all the knowledge required for centralised reasoning. Recently, ALIAS [1] and DARE [9] have shown how to distribute abductive computation in a collaborative system. However, their distributed proof procedures, which are based on the well-known Kakas-Mancarella procedure [6], do not support constructive negation [11] and cannot compute non-ground conditional proofs. Hence they cannot be used for applications such as scheduling and planning involving time and cost, which require constraint processing [12]. In order to overcome this limitation, we propose DAREC, a distributed abductive logic programming framework underpinned by a general and customisable proof procedure that permits collaborative abductive reasoning with constraint processing between decentralised (computational logic) agents. The collaborative reasoning can be seen as a state rewriting process, where the reasoning state, initially containing just the query, is exchanged between the agents. Agents rewrite the state during their local inference by adding relevant (non-ground) assumptions and (dynamically generated) constraints that can then be checked for global consis-