Our research aims at developing interactive, social agents that can coach people to learn new tasks, skills, and habits. In this paper, we focus on coaching sedentary, overweight individuals to exercise regularly. We employ adaptive goal setting in which the coach generates, tracks, and revises personalized exercise goals for a trainee. The goals become incrementally more difficult as the trainee progresses through the training program. Our approach is model-based - the coach maintains a parameterized model of the trainee's aerobic capability that drives its expectation of the trainee's performance. The model is continually revised based on interactions with the trainee. The coach is embodied in a smartphone application which serves as a medium for coach-trainee interaction. We show that our approach can adapt the trainee program not only to several trainees with different capabilities but also to how a trainee's capability improves as they begin to exercise more. Experts rate the goals selected by the coach better than other plausible goals, demonstrating that our approach is effective.

MIT researchers have improved award winning automatic planning software by adding in code that mimics human intuition. The strategies used by high performing human planners were converted into a machine readable form, and then encoded into the automatic planning software. Adding human intuition to the planning software saw an increase in performance between 10 to 15 percent on a challenging set of problems. The research was conducted by scientists at Computer Science and Artificial Intelligence Laboratory (CSAIL), which is known for a number of cutting edge artificial intelligence breakthroughs. The results from the finding will be presented at an upcoming conference of the Association for the Advancement of Artificial Intelligence.

Configuration planning is a form of task planning that takes into consideration both causal and information dependencies in goal achievement. This type of planning is interesting, for instance, in smart home environments which contain various sensors and robots to provide services to the inhabitants. Requests for information, for instance from an activity recognition system, should cause the smart home to configure itself in such a way that all requested information will be provided when it is needed. This paper addresses temporal configuration planning in which information availability and goals are linked to temporal intervals which are subject to constrains. Our solutions are based on constraint-based planning which uses different types of constraints to model different types of knowledge. We propose and compare two approaches to configuration planning. The first one models information via conditions and effects of planning operators and essentially reduces configuration planning to constraint-based temporal planning. The second approach solves information dependencies separately from task planning and optimizes the cost of reaching individual information goals. We compare these approaches in terms of the time it takes to solve problems and the quality of the solutions they provide.

Several advanced applications of autonomous aerial vehicles in civilian and military contexts involve a searching agent with imperfect sensors that seeks to locate a mobile target in a given region. Effectively managing uncertainty is key to solving the related search problem, which is why all methods devised so far hinge on a probabilistic formulation of the problem and solve it through branch-and-bound algorithms, Bayesian filtering or POMDP solvers. In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i.e., a target that is particularly difficult to track down and uncertain to locate. We show that, even for such a complex problem, it is advantageous to compile its probabilistic structure into a deterministic model and use standard deterministic solvers to find solutions. In particular, we formulate the search problem for our uncooperative target both as a deterministic automated planning task and as a constraint programming task and show that in both cases our solution outperforms POMDPs methods.

Goal Recognition Design (GRD) is the problem of designing a domain in a way that will allow easy identification of agents' goals. This work extends the original GRD problem to the Plan Recognition Design (PRD) problem which is the task of designing a domain using plan libraries in order to facilitate fast identification of an agent's plan. While GRD can help to explain faster which goal the agent is trying to achieve, PRD can help in faster understanding of how the agent is going to achieve its goal. We define a new measure that quantifies the worst-case distinctiveness of a given planning domain, propose a method to reduce it in a given domain and show the reduction of this new measure in three domains from the literature.

Bäckström has previously studied a number of optimization problems for partial-order plans, like finding a minimum deordering (MCD) or reordering (MCR), and finding the minimum parallel execution length (PPL), which are all NP-complete. We revisit these problems, but applying parameterized complexity analysis rather than standard complexity analysis. We consider various parameters, including both the original and desired size of the plan order, as well as its width and height. Our findings include that MCD and MCR are W[2]-hard and in W[P] when parameterized with the desired order size, and MCD is fixed-parameter tractable (fpt) when parameterized with the original order size. Problem PPL is fpt if parameterized with the size of the non-concurrency relation, but para-NP-hard in most other cases. We also consider this problem when the number (k) of agents, or processors, is restricted, finding that this number is a crucial parameter; this problem is fixed-parameter tractable with the order size, the parallel execution length and k as parameter, but para-NP-hard without k as parameter.

State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering, which is exact but can be intractable. We propose logical smoothing, a form of backwards reasoning that works in concert with approximated logical filtering to refine past beliefs in light of new observations.  We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation.  We also present an approximation of our smoothing algorithm that is space efficient. We prove properties of our algorithms, and experimentally demonstrate their behaviour, contrasting them with state estimation methods for planning. Smoothing and backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming.

A critical challenge in temporal planning is robustly dealing with non-determinism, e.g., the durational uncertainty of a robot's activity due to slippage or other unexpected influences. Recent advances show that robustness is a better measure of solution quality than traditional metrics such as flexibility. This paper introduces the Robust Execution Problem for finding maximally robust dispatch strategies for general probabilistic temporal planning problems. While generally intractable, we introduce approximate solution techniques — one that can be computed statically prior to the start of execution with robustness guarantees and one that dynamically adjusts to opportunities and setbacks during execution. We show empirically that our dynamic approach outperforms all known approaches in terms of execution success rate.

Monte Carlo Tree Search (MCTS) is a family of directed search algorithms that has gained widespread attention in recent years. Despite the vast amount of research into MCTS, the effect of modifications on the algorithm, as well as the manner in which it performs in various domains, is still not yet fully known. In particular, the effect of using knowledge-heavy rollouts in MCTS still remains poorly understood, with surprising results demonstrating that better-informed rollouts often result in worse-performing agents. We present experimental evidence suggesting that, under certain smoothness conditions, uniformly random simulation policies preserve the ordering over action preferences. This explains the success of MCTS despite its common use of these rollouts to evaluate states. We further analyse non-uniformly random rollout policies and describe conditions under which they offer improved performance.

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM).On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks. Moreover, we show how to encode our framework in an action language so as to perform reachability analysis using virtually any state-of-the-art planner.