Motivated by the success of the translation-based approach for conformant planning, introduced by Palacios and Geffner,  we present two variants of a new compilation scheme from conformant probabilistic planning problems (CPP) to variants of classicalplanning.In CPP, we are given a set of actions -- which we assume to be deterministic in this paper, a distribution over initial states, a goal condition, and a value $0<p\leq 1$. Our task is to find a plan $\pi$ such that the goal probability following the execution of $\pi$ in the initial state is at least $p$. Our firstvariant translates CPP into classicalplanning with resource constraints, in which the resource represents probabilities of failure.  The second variant translates CPPinto cost-optimal classical planning problems, in which costs represents probabilities. Empirically, these techniques show mixed results, performing very well on some domains, and poorly on others. This  indicates that compilation-based technique are a feasible and promising direction for solving CPP problems and, possibly, more general probabilistic planning problems.

The average home computer user needs help in reducing the security risk of their home computer. We are working on an alternative approach from current home security software in which a software agent helps a user manage his/her security risk. Planning is integral to the design of this agent in several ways. First, planning can be used to make the underlying security model manageable by generating attack paths to identify vulnerabilities that are not a problem for a particular user/home computer. Second, planning can be used to identify interventions that can either avoid the vulnerability or mitigate the damage should it occur. In both cases, a central capability is that of generating alternative plans so as to find as many possible ways to trigger the vulnerability and to provide the user with options should the obvious not be acceptable. We describe our security model and our state-based approach to generating alternative plans. We show that the state-based approach can generate more diverse plans than a heuristic-based approach. However, the state-based approach sometimes generates this diversity with better quality at higher search cost.

We are working to develop plan critiquing methods where a planner is used to identify flaws in an existing plan, in order to provide assistance to human planners. In this paper, we describe how to use any classical planning algorithm for verification and counterexample generation for plans already generated by some agent (human or an automated planning system). We show how to take an original classical planning domain, problem, and plan, and a set of uncontrollable (disturbance) actions and agents, and compile those inputs into a new "counter-planning'' problem. This counter-planning problem can be given to an arbitrary PDDL planner, in order to generate counterexample traces where uncontrollable actions can upset plan execution. Our experiments with a large set of planning problems in two multi-agent, dynamic planning domains demonstrated that our approach can verify a plan or generate a counterexample quickly and reliably. We have also compared our approach with a state-of-the-art model-checking system: the results suggest that using classical planners for generating counter plans is more promising than model-checking based verification.

Most of the research in Automated Planning relies on the evaluation of different techniques over a set of benchmarks. The generation of planning tasks for these benchmarks is done using generators coded ad-hoc. Instead, we propose an approach for generating planning problems automatically given the domain definition and some declarative semantics-related information provided by the user. The approach consists of modelling the task of generating planning problems also as a planning problem. The main contribution of this work is that the generation of planning problems is partially handled in a domain-independent way, which leads to a saving of time and effort for researchers. Additionally, the declarative input to the generator facilitates the modification of its behavior. This is a feature of interest for generating different problem distributions.

In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.

Many practical planning problems necessitate the generation of a plan under incomplete information about the state of the world. In this paper we propose the notion of Assumption-Based Planning. Unlike conformant planning, which attempts to find a plan under all possible completions of the initial state, an assumption-based plan supports the assertion of additional assumptions about the state of the world, simplifying the planning problem. In many practical settings, such plans can be of higher quality than conformant plans. We formalize the notion of assumption-based planning, establishing a relationship between assumption-based and conformant planning, and prove properties of such plans. We further provide for the scenario where some assumptions are more preferred than others. Exploiting the correspondence with conformant planning, we propose a means of computing assumption-based plans via a translation to classical planning. Our translation is an extension of the popular approach proposed by Palacios and Geffner and realized in their T0 planner. We have implemented our planner, A0, as a variant of T0 and tested it on a number of expository domains drawn from the International Planning Competition. Our results illustrate the utility of this new planning paradigm.

Classical planning has made huge advances in the last twenty years, leading to solvers able to create plans with thousands of actions for problems described by hundreds of propositions. Yet, the assumptions of classical planning (determinism, model completeness, etc) are oen criticised as being too restrictive to address "real" planning problems. Recently, however, many researchers have started to exploit the good performance of classical planners, through compilations and other methods of reuse, to solve a much wider range of problems. In this way, classical planners have been used for dealing with more expressive planning problems, including incomplete information, temporally extended goals and preferences, as well as to solve problems in various areas of application. Likewise, approaches range from pure compilation (translating a problem into PDDL and solving it with a classical planner) to embedding classical planning techniques inside dedicated algorithms. is body of contributions help illustrate how the results of decades of research in classical planning are now being put to use.

A robot coexisting with humans must not only be able to successfully perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In many social settings, this involves the use of social signals like gaze, facial expression, and language. In this paper we discuss preliminary work focusing on the problem of combining social interaction with task-based action in a dynamic, multiagent bartending domain, using an embodied robot. We discuss how social states are inferred from low-level sensors, using vision and speech as input modalities, and present a planning approach that models task, dialogue, and social actions in a simple bartending scenario. This approach allows us to build interesting plans, which have been evaluated in a real-world study with human subjects, using a general purpose, off-the-shelf planner, as an alternative to more mainstream methods of interaction management.

The most critical moves that determine the success of a manipulation task are performed within the close vicinities of the object prior to grasping, and the goal prior to the final placement. Memorizing these state-action sequences and reusing them can dramatically improve the task efficiency, whereas even the state-of-the-art planning algorithms may require significant amount of time and computational resources to generate a solution from scratch depending on the complexity and the constraints of the task. In this paper, we propose a hybrid approach that combines the reliability of the past experiences gained through demonstration and the flexibility of a generative motion planning algorithm, namely RRT*, to improve task execution efficiency. As a side benefit of reusing these final moves, we can dramatically reduce the number of nodes used by the generative planner, hence the planning time, by either early-terminating the planner when the generated plan reaches a "recalled state", or deliberately biasing it towards the memorized state-action sequences that are feasible at the moment. This complementary combination of the already available partial plans and the generated ones yield to fast, reliable, and repeatable solutions.

In a dynamic environment, an intelligent agent must consider unexpected changes to the world and plan for them. We aim to address this key issue by building more robust artificial agents through the generalization of plan representations. Our research focuses on the process of generalizing various plan forms and the development of a compact representation which embodies a generalized plan as a policy. Our techniques allow an agent to execute efficiently in an online setting. We have, to date, demonstrated our approach for sequential and partial order plans and are pursuing similar techniques for representations such as Hierarchical Task Networks and GOLOG programs