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Alessandro Pinto

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3 papers

2 author rows

AAAI Conference 2016 Conference Paper

Metaphysics of Planning Domain Descriptions

Siddharth Srivastava
Stuart Russell
Alessandro Pinto

STRIPS-like languages (SLLs) have fostered immense advances in automated planning. In practice, SLLs are used to express highly abstract versions of real-world planning problems, leading to more concise models and faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to SLL models that are unsolvable, SLL models whose solutions are incorrect with respect to the real-world problem, or models that are inexpressible in SLLs. There is some evidence that such limitations have restricted the applicability of AI planning technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power—for example, allowing angelic nondeterminism in action effects—and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.

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ICRA Conference 2014 Conference Paper

Hierarchical Multi-objective planning: From mission specifications to contingency management

Xuchu Dennis Ding
Brendan J. Englot
Alessandro Pinto
Alberto Speranzon
Amit Surana

We propose a hierarchical planning framework for mission planning and execution in uncertain and dynamic environments. We consider missions that involve motion planning in large, cluttered environments, trading off mission objectives while satisfying logical/spatial/temporal constraints. Our framework enables the decomposition of the planning problem across different layers, leveraging the difference in spatial and temporal scales of the mission objectives. We show that this framework facilitates contingency management under unanticipated events. Interaction between the various layers requires consistent model abstractions and common message semantics. To satisfy these requirements, we adopt a generic knowledge-based architecture that is independent from a specific application domain. We show a specific instance of our framework using a Constrained Markov Decision Process (CMDP) planner at the higher level and a Multi-Objective Probabilistic Roadmap (MO-PRM) planner at the lower level. The resulting planning system is tested in a realistic scenario where an agent is tasked with a mission in a large urban threat rich environment under dynamic uncertain conditions. The mission specification includes a Linear Temporal Logic (LTL) formula that defines the desired behaviors, a list of metrics to be optimized and a list of constraints on time, resources and probability of mission success.

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ICRA Conference 2013 Conference Paper

Strategic planning under uncertainties via constrained Markov Decision Processes

Xu Chu Ding
Alessandro Pinto
Amit Surana

In this paper, we propose a hierarchical mission planner where the state of the world and of the mission are abstracted into corresponding states of a Markov Decision Process (MDP). Transitions in the MDP represent abstract motion actions that are planned by a lower level probabilistic planner. The cost structure of the MDP is multi-dimensional: each state-action pair is annotated with a vector of metrics such as time and resource requirements. A mission specification is divided into three parts: a temporal logic formula defined over state propositions, the choice of the primary cost, and constraints on the remaining secondary costs. The planning problem is formulated as finding the optimal policy of a Constrained Markov Decision Process with above mission specification. The resulting planning system is tested in a mission where an agent is tasked with a complex mission in a urban hostile environment.

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