KQML as an agent communication language
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Applications of abduction: knowledge-level modelling
International Journal of Human-Computer Studies
Solving the frame problem: a mathematical investigation of the common sense law of inertia
Solving the frame problem: a mathematical investigation of the common sense law of inertia
Database Updates through Abduction
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Agent-based distributed software verification
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Multi-threaded communicating agents in qu-prolog
CLIMA'05 Proceedings of the 6th international conference on Computational Logic in Multi-Agent Systems
LAILA: a language for coordinating abductive reasoning among logic agents
Computer Languages
Multi-agent planning with confidentiality
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
SAGE: A Logical Agent-Based Environment Monitoring and Control System
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Distributed abductive reasoning with constraints
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Abduction of distributed theories through local interactions
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
25 years of applications of logic programming in Italy
A 25-year perspective on logic programming
Speculative abductive reasoning for hierarchical agent systems
CLIMA'10 Proceedings of the 11th international conference on Computational logic in multi-agent systems
Distributed abductive reasoning with constraints
DALT'10 Proceedings of the 8th international conference on Declarative agent languages and technologies VIII
Towards efficient multi-agent abduction protocols
LADS'10 Proceedings of the Third international conference on Languages, methodologies, and development tools for multi-agent systems
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Abductive reasoning is a well established field of Artificial Intelligence widely applied to different problem domains not least cognitive robotics and planning. It has been used to abduce high-level descriptions of the world from robot sense data, using rules that tell us what sense data would be generated by certain objects and events of the robots world, subject to certain constraints on their co-occurrence. It has also been used to abduce actions that might result in a desired goal state of the world, using descriptions of the normal effects of these actions, subject to constraints on the action combinations. We can generalise these applications to a multi-agent context. Several robots can collaboratively try to abduce an agreed higher-level description of the state of the world from their separate sense data consistent with their collective constraints on the abduced description. Similarly, multi-agent planning can be accomplished by the abduction of the actions of a collective plan where each agent uses its own description of the effect of its actions within the plan, such that the constraints on the actions of all the participating agents are satisfied. To address this class of problems, we need to generalise the single agent abductive reasoning algorithm to a distributed abductive inference algortihm. In addition, if we want to investigate applications in which the set of collaborating robots/agents is open, we need an algorithm that allows agents to join or leave the collaborating group whilst a particular inference is under way, but which still produces sound abductive inferences. This paper describes such a distributed abductive reasoning system, which we call DARE, and its implementation in the multi-threaded Qu-Prolog variant of Prolog. We prove the soundness of the algorithm it uses and we discuss its completeness in relation to non-distributed abductive reasoning. We illustrate the use of the algorithm with a multi-agent meeting scheduling example. The task is open in that the actual agents who need to attend is not determined in advance. Each individual agent has its own constraints on the possible meeting time and concerning which other agents must or must attend the meeting, if it attends. The algorithm selects the agents to attend and ensures that the constraints of each of the attending agents are satisfied.