Systems that detect and repair their own mistakes

Making mistakes is an inescapable aspect of everyday life. We constantly make mistakes, recognize them and try to correct them. Mistakes are inevitable because of the incompleteness of our knowledge of the world, its inherent uncertainty and its being in a constant state of change. We can never know for sure that what we know is true and the actions that we take based on these beliefs can therefore be misguided. Sooner or later we act based on some false belief or the world changes in an unexpected way and we fail to achieve our goal. But the fact that we can recognize and repair these errors mitigates their effects. Software systems face the same problems. The difference is that they do not usually have as robust a capability as we have to detect and respond to their mistakes. This is part of what makes them seem brittle and user-unfriendly. This problem is not likely to get any better as the systems exhibit more complex behaviors in more realistic domains.; Our work begins to address that problem by focusing on the computational capabilities required of software systems for them to be able to autonomously recognize and respond to their own mistakes. We study in particular, mistaken beliefs, intentions and actions in agents that have some goals to achieve. Intuitively enough, the basic capabilities required are an ability to inspect their past behavior and computations and the past states of the world and to use that to determine their future behavior. These abilities are not typically available in software systems.; We have implemented a general logical framework in which one can specify the behavior of an agent that supports this kind of representation and computation. We have implemented agents that detect and respond appropriately to their mistakes in some aspects of language processing. We have also implemented a system that handles its mistaken beliefs in any domain that can be described using the language of non-monotonic logic. This system was tested on a test suite that we compiled from examples of non-monotonic reasoning in the literature. We finally provide a design of the representations and algorithms for handling mistakes in an agent that acts in the world and has mistaken beliefs, intentions and actions. Implementing such an agent is the next step in this work.