In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required that each state on the trajectory induced by the execution of plans complies with a given set of global constraints. We approach the computation of plans for such domains as the problem of searching over a deterministic state model. In this model, some of the successor states are obtained by solving numerically the so-called initial value problem over a set of ordinary differential equations (ODE) given by the current plan prefix. These equations hold over time intervals whose duration is determined dynamically, according to whether zero crossing events take place for a set of invariant conditions. The resulting planner, FS+, incorporates these features together with effective heuristic guidance. FS+ does not impose any of the syntactic restrictions on process effects often found on the existing literature on Hybrid Planning. A key concept of our approach is that a clear separation is struck between planning and simulation time steps. The former is the time allowed to observe the evolution of a given dynamical system before committing to a future course of action, whilst the later is part of the model of the environment. FS+ is shown to be a robust planner over a diverse set of hybrid domains, taken from the existing literature on hybrid planning and systems.

Utrip, a Seattle startup that uses machine learning to help travelers plan their trips, just closed a $4 million funding round. Investors in the Series A round include Plug and Play, Tiempo Capital, Acorn Ventures, and executives from companies such as Apple and Costco, participating as angel investors. The cash will go toward Utrip's machine learning and data science operations, which fuel the platform's recommendation engine. "One of the things that our travelers love about Utrip is the depth with which we curate destinations and go beyond those top 10 lists that are available everywhere to offer experiences that are really unique and local and authentic for that destination," said Utrip CEO Gilad Berenstein. "That's one big priority, continuing to build out our machine learning capabilities as well as our human expert network, our chefs, artists, historians, etcetera."

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.

In recent years, there has been growing interest in the study of automated playlist generation — music recommender systems that focus on modeling preferences over song sequences rather than on individual songs in isolation. This paper addresses this problem by learning personalized models on the fly of both song and transition preferences, uniquely tailored to each user’s musical tastes. Playlist recommender systems typically include two main components: i) a preference-learning component, and ii) a planning component for selecting the next song in the playlist sequence. While there has been much work on the former, very little work has been devoted to the latter. This paper bridges this gap by focusing on the planning aspect of playlist generation within the context of DJ-MC, our playlist recommendation application. This paper also introduces a new variant of playlist recommendation, which incorporates the notion of diversity and novelty directly into the reward model. We empirically demonstrate that the proposed planning approach significantly improves performance compared to the DJ-MC baseline in two playlist recommendation settings, increasing the usability of the framework in real world settings.

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.

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-as-planning 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.

It has been shown recently that the performance of greedy best-first search (GBFS) for computing plans that are not necessarily optimal can be improved by adding forms of exploration when reaching heuristic plateaus: from random walks to local GBFS searches. In this work, we address this problem but using structural exploration methods resulting from the ideas of width-based search. Width-based methodsseek novel states, are not goal oriented, and their power has been shown recently in the Atari and GVG-AI video-games. We show first that width-based exploration in GBFS is more effective than GBFS with local GBFS search (GBFS-LS), and then proceed to formulate a simple and general computational framework where standard goal-oriented search (exploitation) and width-based search (structural exploration) are combined to yield a search scheme, best-first width search, that is better than both and which results in classical planning algorithms that outperform the state-of-the-art planners.

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.

State-of-the-art methods for solving SSPs often work by limiting planning to restricted regions of the state space. The resulting problems can then be solved quickly, and the process is repeated during execution when states outside the restricted region are encountered. Typically, these approaches focus on states that are within some distance measure of the start state (e.g., number of actions or probability of being reached). However, these short-sighted approaches make it difficult to propagate information from states that are closer to a goal than to the start state, thus missing opportunities to improve planning. We present an alternative approach in which short-sightedness is used only to determine whether a state should be labeled as solved or not, but otherwise the set of states that can be accounted for during planning is unrestricted. Based on this idea, we propose the FLARES algorithm and show that it performs consistently well on a wide range of benchmark problems.

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.