text: In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities. In this paper, we consider the problem where an autonomous agent needs to act in a manner that clarifies its objectives to cooperative entities while preventing adversarial entities from inferring those objectives. We show that this problem is solvable when cooperative entities and adversarial entities use different types of sensors and or prior knowledge. We develop two new solution approaches for computing such plans. One approach provides an optimal solution to the problem by using an IP solver to provide maximum obfuscation for adversarial entities while providing maximum legibility for cooperative entities in the environment, whereas the other approach provides a satisficing solution using heuristic-guided forward search to achieve preset levels of obfuscation and legibility for adversarial and cooperative entities respectively. We show the feasibility and utility of our algorithms through extensive empirical evaluation on problems derived from planning benchmarks.
  @cite_18: A multispot welding machine for producing a mesh web from arrays of wire running skew to the longitudinal direction of the web and crossing one another has two endless synchronously circulating feeders which carry deflector-pins and each of which has a straight working run; the runs are uniformly spaced from one another and each defining a different one of the edges of the mesh web to be produced, a wire-layer which is movable to and fro across the feeders and arranged to lay perpendicular to the runs a number of wires alternately about a corresponding number of deflector-pins on the working runs of the two feeders, and a welding zone having means for welding the wires together at their crossover points. The wire-layer has an auxiliary drive mechanism which is capable of imparting to the wire-layer, each time after the wire-layer crosses over the working run of a feeder, an additional rapid forwards motion in a direction parallel to the working runs and subsequent backwards motion so as to form loops of the wire around the deflector-pins.
  @cite_22: Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently, without the need of a plan library, using slightly modified planning algorithms. In this work, we extend this approach to the more general problem of probabilistic plan recognition where a probability distribution over the set of goals is sought under the assumptions that actions have deterministic effects and both agent and observer have complete information about the initial state. We show that this problem can be solved efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal probabilities, are computed by means of two calls to a classical planner that no longer has to be modified in any way. A number of examples is considered to illustrate the quality, flexibility, and scalability of the approach.
  @cite_6: In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G* of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goal G belongs to G* if there is an action sequence π that is an optimal plan for both the goal G and the goal G extended with extra goals representing the observations. Exploiting this result, we show how the set G* can be computed exactly and approximately by minor modifications of existing optimal and suboptimal planning algorithms, and existing polynomial heuristics. Experiments over several domains show that the suboptimal planning algorithms and the polynomial heuristics provide good approximations of the optimal goal set G* while scaling up as well as state-of-the-art planning algorithms and heuristics.
  @cite_0: 
  @cite_16: Knowing a user's plans and goals can significantly improve the effectiveness of an interactive system. However, recognizing such goals and the user's intended plan for achieving them is not an easy task. Although much research has dealt with representing the knowledge necessary for plan inference and developing strategies that hypothesize the user's evolving plans, a number of serious problems still impede the use of plan recognition in large-scale, real-world applications. This paper describes the various approaches that have been taken to plan inference, along with techniques for dealing with ambiguity, robustness, and representation of requisite domain knowledge, and discusses areas for further research.
  @cite_10: Goal Recognition is the task of inferring an actor's goals given some or all of the actor's observed actions. There is considerable interest in Goal Recognition for use in intelligent personal assistants, smart environments, intelligent tutoring systems, and monitoring user's needs. In much of this work, the actor's observed actions are compared against a generated library of plans. Recent work by Ramirez and Geffner makes use of AI planning to determine how closely a sequence of observed actions matches plans for each possible goal. For each goal, this is done by comparing the cost of a plan for that goal with the cost of a plan for that goal that includes the observed actions. This approach yields useful rankings, but is impractical for real-time goal recognition in large domains because of the computational expense of constructing plans for each possible goal. In this paper, we introduce an approach that propagates cost and interaction information in a plan graph, and uses this information to estimate goal probabilities. We show that this approach is much faster, but still yields high quality results.
summary: This work is connected to the plan recognition literature, since it involves intended recognition @cite_18 @cite_16 , that is, the actor is aware of the observers in the environment, and takes actions that either explicitly convey information or hide information from the observers. Then the observers can use the emitted observations to perform goal recognition. There are several prior works on goal plan recognition @cite_0 @cite_22 @cite_10 @cite_6 . However, most of these recognition systems assume one-to-one mapping of observation symbols to actions or states, which complicates the goal recognition when the sensor model supports noisy many-to-one mapping of observations. In our framework, we assume the observer constructs a sequence of belief updates to derive a list of possible goals in the final belief. In the adversarial case, this list does not reveal information about the actor's true goal, whereas, in the cooperative case, this list is indeed what the actor wants to convey to the observer.