In this work we present a theoretical approach (not currently implemented), to the problem of predicting agent behavior. The ultimate goal of this work is to learn models that can be used to predict the future actions of intelligent agents, based on previously recorded data on those agents’ behavior. We believe that we can improve the predictive accuracy of our models by assuming that an agent reasons about the actions it takes, and trying to explicitly model that reasoning process. Here, we model an agent’s reasoning process as a form of Monte-Carlo search, and attempt to learn a state evaluation function that, when used with this planning algorithm, yields a similar distribution of actions given the current state of the world as we observe in the data. While it is simple to simulate Monte-Carlo search given an evaluation function, it is much more difficult to determine an evaluation function that will generate a certain behavior. Here we will use Expectation-Maximization to find a maximum likelihood estimate of the parameters of the evaluation function, treating the actual steps taken in planning each action as unobserved data.

Our research focuses on the use of Planning & Scheduling (P&S) technology for a team of robots providing daily assistance to multiple elder adults living in retirement facilities. Multi-user assistance and group-based activities require robots to plan and schedule their human-robot interaction (HRI) activities based on the specific needs, time constraints, availability and preferences of the multiple users. In this paper, we introduce and implement a novel centralized system architecture that can manage real P&S scenarios with multiple socially assistive robots, multiple users and their individual schedules, and single- and multi-person assistive activities. We describe how the main components of the proposed P&S architecture are integrated to control the robots, and to generate and monitor sequences of temporally annotated activities using off-the-shelf temporal planners. We verify that the architecture can manage realistic scenarios with three assistive robots, twenty users, and several single- and group-based activity requests during a single day.

Recent increase in interest in using rotorcraft (helicopters and tilt-rotor craft) for public transportation has spurred research in making rotorcraft less noisy, particularly as they land. The ground noise associated with landing trajectories followed by rotorcraft depends in part on the changes in altitude and velocity of the rotorcraft during flight. Acoustic models of ground noise taking altitude and velocity effects into account can be used in an optimization process to determine a set of potentially quieter pilot operations. However, optimizing solely for acoustic properties produces patterns that abstract away from the environment in which the trajectory is flown. A quiet procedure flown over a residential area can create considerable annoyance. To overcome this limitation of acoustic-based optimization we propose a hybrid cost model for optimization that combines acoustic criteria with a land use model that views noise-sensitive areas around landing facilities as weighted obstacles. The result is a 3D route planning problem with obstacles. We introduce a system, called NORA (Noise Optimization for Rotorcraft Approach) that allows for the computation of trajectories that simultaneously solve for acoustically quiet patterns that also avoid land sensitive areas.

We introduce an application of self-driving vehicle technology to the problem of towing aircraft at busy airports from gate to runway and runway to gate. Autonomous towing can be supervised by human ramp- or ATC controllers, pilots, or ground crew. The controllers provide route information to the tugs, assisted by an automated route planning system. The planning system and tower and ground controllers work in conjunction with the tugs to make tactical decisions during operations to ensure safe and effective taxiing in a highly dynamic environment. We argue here for the potential for significantly reducing fuel emissions, fuel costs, and community noise, while addressing the added complexity of air terminal operations by increasing efficiency and reducing human workload. This paper describes work-in-progress for developing concepts and capabilities for autonomous engines-off taxiing using towing vehicles.

Energy consumption in commercial and educational buildings is impacted by group activities such as meetings, workshops, classes and exams, and can be reduced by scheduling these activities to take place at times and locations that are favorable from an energy standpoint. This paper improves on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. To scale up to realistic problem sizes, we embed this MILP model into a large neighbourhood search (LNS). We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approaches.

Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a knowledge representation that is useful in mechanizing decision theoretic planning in relational domains. GFODDs generalize function-free first order logic and include numerical values and numerical generalizations of existential and universal quantification. Previous work presented heuristic inference algorithms for GFODDs and implemented these heuristics in systems for decision theoretic planning. In this paper, we study the complexity of the computational problems addressed by such implementations. In particular, we study the evaluation problem, the satisfiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability. Our results provide a complete characterization placing these problems within the polynomial hierarchy. The same characterization applies to the corresponding restriction of problems in first order logic, giving an interesting new avenue for efficient inference when the number of objects is bounded. Our results show that for $\Sigma_k$ formulas, and for corresponding GFODDs, evaluation and satisfiability are $\Sigma_k^p$ complete, and equivalence is $\Pi_{k+1}^p$ complete. For $\Pi_k$ formulas evaluation is $\Pi_k^p$ complete, satisfiability is one level higher and is $\Sigma_{k+1}^p$ complete, and equivalence is $\Pi_{k+1}^p$ complete.

The strategy has been implemented in a simple QLISP program.

This is the second of two papers about MOLGEN.

A plan for a question required the composition of REQUEST and INFORM and led to the development of two new kinds of informing speech acts, INFORMREF To plan a yes/no question about some proposition P. one should think that the and INFORMIF, and their mediating acts. The INFORMREF acts lead to hearer knows whether P is true or false (or, at least "might know"). An approximate "what," "when," and "where" questions while INFORMIF results in a yes/no representation of AGT2's knowing whether P is true or false is OR (AGT2 question.2' The reason for these new acts is that, in planning a REQUEST that BELIEVE P, AGT2 BELIEVE -- P)).'9 Such goals are often created, as modelled someone else perform an INFORM act, one only has incomplete knowledge of by our type 4 inference, when a planner does not know the truth-value of P. their beliefs and goals; but an INFORM, as originally defined can only be Typical circumstances in which an agent may acquire such disjunctive beliefs planned when one knows what is to be said.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE