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Deepak Khemani

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4 papers

2 author rows

AAAI Conference 2021 Short Paper

Mental Actions and Explainability in Kripkean Semantics: What Else do I Know? (Student Abstract)

Shikha Singh
Deepak Khemani

The ability of an agent to distinguish the ramification effects of an action from its direct effects adds value to the explainability of its decisions. In this work, we propose to encode the ramification effects of ontic and epistemic actions as single-point update models in an epistemic planning domain modeled with Kripkean semantics of Knowledge and Belief. We call them “mental actions”. We discuss a preliminary approach to realize our idea, and we conclude by pointing out some optimizations as our ongoing pursuit.

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AAAI Conference 2018 Conference Paper

Content and Context: Two-Pronged Bootstrapped Learning for Regex-Formatted Entity Extraction

Stanley Simoes
Deepak P
Munu Sairamesh
Deepak Khemani
Sameep Mehta

Regular expressions are an important building block of rulebased information extraction systems. Regexes can encode rules to recognize instances of simple entities which can then feed into the identiﬁcation of more complex cross-entity relationships. Manually crafting a regex that recognizes all possible instances of an entity is difﬁcult since an entity can manifest in a variety of different forms. Thus, the problem of automatically generalizing manually crafted seed regexes to improve the recall of IE systems has attracted research attention. In this paper, we propose a bootstrapped approach to improve the recall for extraction of regex-formatted entities, with the only source of supervision being the seed regex. Our approach starts from a manually authored high precision seed regex for the entity of interest, and uses the matches of the seed regex and the context around these matches to identify more instances of the entity. These are then used to identify a set of diverse, high recall regexes that are representative of this entity. Through an empirical evaluation over multiple real world document corpora, we illustrate the effectiveness of our approach.

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ICAPS Conference 2006 Conference Paper

Planning for PDDL3 - An OCSP Based Approach

Bharat Ranjan Kavuluri
Naresh Babu Saladi
Deepak Khemani

Recent research in AI Planning is focused on improving the quality of the generated plans. PDDL3 incorporates hard and soft constraints on goals and the plan trajectory. Plan trajectory constraints are conditions that need to be satisfied at various stages of the plan. Soft goals are goals, which need not necessarily be achieved but are desirable. An extension of Constraint Satisfaction Problem, called Optimal Constraint Satisfaction Problem (OCSP) has allowance for defining soft constraints and objective functions. Each soft constraint is associated with a penalty, which will be levied if the constraint is violated. The OCSP solver arrives at a solution that minimizes the total penalty (Objective function) and satisfies all hard constraints. In this paper, an OCSP encoding for the classical planning problems with plan trajectory constraints, soft and hard goals is proposed. Modal operators associated with hard goals and hard plan trajectory constraints are handled by preprocessing and imposing new constraints over the existing GP-CSP encoding. A new encoding for each of the modal operators associated with the soft goals and soft plan trajectory constraints is proposed. Also, a way of encoding conditional goal preference constraints into OCSP is discussed. Based on this research, we intend to submit a planner for the coming planning competition — IPC2006.

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ICAPS Conference 1994 Conference Paper

Planning with Thematic Actions

Deepak Khemani

Thetask of planning in a dynamicand uncertain domainis considerablymorechallengingthan in domainstraditionally adopted by classical planning methods. Planning in real situations has to be 8 knowiedp intm ptoceu, particudarly sinceit is noteasyto predictall theeffectsof one’sactions. However, manyknowledge bated implementations are susceptible to brittleness. Contractbridge oReaa domainin whichmany of the issuesinvolvedin real worldproblemscan be addreued without having to make simplifications in rapretentation, planningin the lameof bridp takesus away fromthe traditiona! searchhasedmethods (like the alpha-beta procedure), whichare app]icable in oomplatednformation gamesUk¢chess. In this paperwelook at a moreflexible use of knowledge structures for planningin bridge.

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