Comirit: Commonsense Reasoning by Integrating Simulation and Logic
نویسندگان
چکیده
Rich computer simulations or quantitative models can enable an agent to realistically predict real-world behavior with precision and performance that is difficult to emulate in logical formalisms. Unfortunately, such simulations lack the deductive flexibility of techniques such as formal logics and so do not find natural application in the deductive machinery of commonsense or general purpose reasoning systems. This dilemma can, however, be resolved via a hybrid architecture that combines tableaux-based reasoning with a framework for generic simulation based on the concept of ‘molecular’ models. This combination exploits the complementary strengths of logic and simulation, allowing an agent to build and reason with automatically constructed simulations in a problem-sensitive manner.
منابع مشابه
Conservative and Reward-driven Behavior Selection in a Commonsense Reasoning Framework
Comirit is a framework for commonsense reasoning that combines simulation, logical deduction and passive machine learning. While a passive, observation-driven approach to learning is safe and highly conservative, it is limited to interaction only with those objects that it has previously observed. In this paper we describe a preliminary exploration of methods for extending Comirit to allow safe...
متن کاملThe Toy Box Problem (and a Preliminary Solution)
The evaluation of incremental progress towards ‘Strong AI’ or ‘AGI’ remains a challenging open problem. In this paper, we draw inspiration from benchmarks used in artificial commonsense reasoning to propose a new benchmark problem— the Toy Box Problem—that tests the practical real-world intelligence and learning capabilities of an agent. An important aspect of a benchmark is that it is realisti...
متن کاملOn Integrating Inductive Learning with Prior Knowledge and Reasoning
Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of neural networks and machine learning, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning share many interdependencies, and the integration of the two may lead to more powerful models. This dis...
متن کاملPutting People's Common Sense into Knowledge Bases of Household Robots
Unlike people, household robots cannot rely on commonsense knowledge when accomplishing everyday tasks. We believe that this is one of the reasons why they perform poorly in comparison to humans. By integrating extensive collections of commonsense knowledge into mobile robot’s knowledge bases, the work proposed in this paper enables robots to flexibly infer control decisions under changing envi...
متن کاملDeeper Understanding of Vague Instructions through Simulated Execution
A commonsense understanding of the physical world will be crucial for the robots of the future as they strive to perform everyday activities and instructions formulated by human users in natural language. One mechanism that is believed to assist human cognition in commonsense reasoning is mental simulation, the envisioning of actions before they are performed. We therefore present a system inte...
متن کامل