Mīmāṁsā, an Indian hermeneutics provides an exhaustive methodology to interpret Vedic statements. A formalism namely, Mīmāṁsā Inspired Representation of Actions has already been proposed in a preliminary manner. This paper expands the formalism logically and includes Syntax and Semantics covering Soundness and Completeness. Here, several interpretation techniques from Mīmāṁsā have been considered for formalising the statements. Based on these, instructions that denote actions are categorized into positive and prohibitive unconditional imperatives and conditional imperatives that enjoin reason, temporal action and goal. (...) These instructions are evaluated to three values S, V and N. If the instruction is executed with the intention of goal, then it acquires the value of S; if the intention of goal is present and the instruction is not executed, then it assumes the value of V; and if there is no intention of goal, then the instruction is evaluated to N. This formalism has also been applied in different computational settings such as AI planning, Robotics, task analysis and classification of natural language instructions. An outline of these applications and the comparison of this formalism with other relevant theories are also presented in this paper. (shrink)

. This paper endeavors to formalize imperatives that convey actions. Imperatives, unlike propositions, do not hold the value of true or false. Peter Vranas proposed an alternate logical formalism in the literature of imperative logic with three values, namely: Satisfaction (), Violation () and Avoidance (). Although this formalism takes into account the conditional imperatives, it does not address imperatives from the perspective of actions. According to Mīmāṁsā, the prime motive of an imperative is to carry out action so as (...) to reach the goal. This paper modifies the notion of Vranas' imperative logic using Mīmāṁsā principles. The resulting imperative formalism, focusing on action representation, is presented with syntax, deduction rules and semantic tables. The effectiveness of this representation is demonstrated in the area of artificial intelligence planning. (shrink)