Action Language Hybrid AL

This paper introduces an extension of the action language AL to Hybrid AL. A program in Hybrid AL specifies both a transition diagram and associated computations for observing fluents and executing actions. The semantics of AL is defined in terms of Answer Set Programming (ASP). Similarly, the semantics of Hybrid AL is defined using Hybrid ASP which is an extension of ASP that allows rules to control sequential execution of arbitrary algorithms. Constructing a mathematical model of an agent and its environment based on the theory of action languages has been studied and has applications to planning and diagnostic problems, see [10] for an overview. In the realm of diagnostic problems, the goal is to find explanations of unexpected observations. We are interested in solving diagnostic problems such as those that arise diagnosing malfunctions of a large distributed software system, as described in [14]. The approach to solving a diagnostic problems described in [1] is based on the idea of using a mathematical model of the agent’s domain, created using a description in the action language AL [2] to find explanations for unexpected observations. Central to this approach is the notion of the agent loop [10] which we modify to underline the relevance to the diagnostic problem. 1. Observe the world, check that observations are consistent with expectations, and update the knowledge base. 2. Select an appropriate goal G. 3. Explain unexpected observations and search for a plan (a sequence of actions) to achieve G. 4. Execute an initial part of the plan, update the knowledge base, go back to step 1. The description and the facts from the knowledge base are translated into a logic program in a language of answer set programming (ASP) [11]. An ASP solver is then used to find stable models of the program, which are descriptions of possible trajectories of the underlying domain. These can be used to carry out steps 1 and 3 of the agent loop. The two assumptions for the applicability of the agent loop are: (1) the agent is capable of making correct observations, performing actions, and recording these observations and actions (not defeasible), and (2) normally the agent is capable of observing all relevant exogenous actions occurring in its environment (defeasible). Hybrid AL is introduced to help solve a diagnostic problem where both (1) and (2) are defeasible, and where to decrease the size of the search space