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Anton Riabov

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5 papers

1 author row

AAAI Conference 2018 Conference Paper

An AI Planning Solution to Scenario Generation for Enterprise Risk Management

Shirin Sohrabi
Anton Riabov
Michael Katz
Octavian Udrea

Scenario planning is a commonly used method by companies to develop their long-term plans. Scenario planning for risk management puts an added emphasis on identifying and managing emerging risk. While a variety of methods have been proposed for this purpose, we show that applying AI planning techniques to devise possible scenarios provides a unique advantage for scenario planning. Our system, the Scenario Planning Advisor (SPA), takes as input the relevant information from news and social media, representing key risk drivers, as well as the domain knowledge and generates scenarios that explain the key risk drivers and describe the alternative futures. To this end, we provide a characterization of the problem, knowledge engineering methodology, and transformation to planning. Furthermore, we describe the computation of the scenarios, lessons learned, and the feedback received from the pilot deployment of the SPA system in IBM.

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AAAI Conference 2017 Conference Paper

State Projection via AI Planning

Shirin Sohrabi
Anton Riabov
Octavian Udrea

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-asplanning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of ﬁnding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.

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AAAI Conference 2015 Conference Paper

Towards Cognitive Automation of Data Science

Alain Biem
Maria Butrico
Mark Feblowitz
Tim Klinger
Yuri Malitsky
Kenney Ng
Adam Perer
Chandra Reddy

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e. g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.

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AAAI Conference 2013 Conference Paper

Hypothesis Exploration for Malware Detection Using Planning

Shirin Sohrabi
Octavian Udrea
Anton Riabov

In this paper we apply AI planning to address the hypothesis exploration problem and provide assistance to network administrators in detecting malware based on unreliable observations derived from network traffic. Building on the already established characterization and use of AI planning for similar problems, we propose a formulation of the hypothesis generation problem for malware detection as an AI planning problem with temporally extended goals and actions costs. Furthermore, we propose a notion of hypothesis “plausibility” under unreliable observations, which we model as plan quality. We then show that in the presence of unreliable observations, simply finding one most “plausible” hypothesis, although challenging, is not sufficient for effective malware detection. To that end, we propose a method for applying a stateof-the-art planner within a principled exploration process, to generate multiple distinct high-quality plans. We experimentally evaluate this approach by generating random problems of varying hardness both with respect to the number of observations, as well as the degree of unreliability. Based on these experiments, we argue that our approach presents a significant improvement over prior work that are focused on finding a single optimal plan, and that our hypothesis exploration application can motivate the development of new planners capable of generating the top high-quality plans.

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AAAI Conference 2005 Conference Paper

Planning for Stream Processing Systems

Anton Riabov

With the advent of compositional programming models in computer science, applying planning technologies to automatically build workflows for solving large and complex problems in such a paradigm becomes not only technically appealing but also feasible approach. The application areas that will benefit from automatic composition include, among others, Web services, Grid computing and stream processing systems. Although the classical planning formalism is expressive enough to describe planning problems that arise in a large variety of different applications, it can pose significant limitations on planner performance in compositional applications, in particular, in stream processing systems. In this paper we extend the classical planning formalism by introducing new language constructs that support the structure of stream processing domains. Exposing this structure to the planner can result in dramatic performance improvements: our experiments show exponential planning time reduction in comparison to most recent metric planners.

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