Planning, Execution and Learning in a Robotic Agent
This paper presents the complete integrated planning, executing and learning robotic agent ROGUE. We describe ROGUE'S task planner that interleaves high-level task planning with real world robot execution. It supports multiple, asynchronous goals, suspends and interrupts tasks, and monitors and compensates for failure. We present a general approach for learning situation-dependent rules from execution, which correlates environmental features with learning opportunities, thereby detecting patterns and allowing planners to predict and avoid failures. We present two implementations of the general learning approach, in the robot's path planner, and in the task planner. We present empirical data to show the effectiveness of ROGUE'S novel learning approach.

title: Motion Control in Dynamic Multi-Robot Environments
abstract: This paper presents the motion control system used by CMUnited-98, the small-size league champion at RoboCup-98. The robotic soccer team consists of five robots that aim at achieving specific goals while navigating in a limited space shared with the five other opponent robots. We introduce our motion control algorithm, which allows a general differential-driven robot to accurately reach a target point with a desired orientation in an environment with multiple moving obstacles. We describe how the features of our motion controller help to build interesting and robust behaviors. We also briefly compare our system to other motion control techniques and include descriptions and illustrations of the performance of our fully-implemented motion control algorithm.

title: Route Planning by Analogy
abstract: There have been several efforts to create and use real maps in computer applications that automatically find good map routes. In general, online map representations do not include information that may be relevant for the purpose of generating good realistic routes, including for example traffic patterns, construction, or number of lanes. Furthermore, the notion of a good route is dependent on a variety of factors, such as the time of the day, and may also be user dependent. This motivation leads to our work on the accumulation and reuse of previously traversed routes as cases. In this paper, we demonstrate our route planning method which retrieves and reuses multiple past routing cases that collectively form a good basis for generating a new routing plan. We briefly present our similarity metric for retrieving a set of similar routes. The metric effectively takes into account the geometric and continuous-valued characteristics of a city map. We then present the replay mechanism and how the planner produces the route plan by analogizing from the retrieved similar past routes. We discuss in particular the strategy used to merge a set of cases and generate the new route. We use illustrative examples and show some empirical results from a detailed online map of the city of Pittsburgh containing over 18,000 intersections and 25,000 street segments.

title: SetA*: an efficient BDD-based heuristic search algorithm
abstract: In this paper we combine the goal directed search of A* with the ability of BDDs to traverse an exponential number of states in polynomial time. We introduce a new algorithm, SetA*, that generalizes A* to expand sets of states in each iteration. SetA* has substantial advantages over BDDA*, the only previous BDD-based A* implementation we are aware of. Our experimental evaluation proves SetA* to be a powerful search paradigm. For some of the studied problems it outperforms BDDA*, A*, and BDD-based breadth-first search by several orders of magnitude. We believe exploring sets of states to be essential when the heuristic function is weak. For problems with strong heuristics, SetA* efficiently specializes to single-state search and consequently challenges single-state heuristic search in general.

title: Exploiting domain geometry in analogical route planning
abstract: Automated route planning consists of using real maps to automatically find good map routes. Two shortcomings to standard methods are (1) that domain information may be lacking, and (2) that a ‘good’ route can be hard to define. Most on-line map representations do not include information that may be relevant for the purpose of generating good realistic routes, such as traffic patterns, construction, and one-way streets. The notion of a good route is dependent not only on geometry (shortest path),but also on a variety of other factors, such as the day and time, weather conditions,and perhaps most importantly,user-dependent preferences. These features can be learned by evaluating real-world execution experience. These difficulties motivate our work on applying analogical reasoning to route planning. Analogical reasoning is a method of using past experience to improve problem solving performance in similar new situations.Our approach consists of the accumulation and reuse of previously traversed rou...

title: Using decision tree confidence factors for multi-agent control
abstract: Although Decision Trees are widely used for classification tasks, they are typically not used for agent control. This paper presents a novel technique for agent control in a complex multiagent domain based on the confidence factors provided by the C4.5 Decision Tree algorithm. Using Robotic Soccer as an example of such a domain, this paper incorporates a previously-trained Decision Tree into a full multiagent behavior that is capable of controlling agents throughout an entire game. Along with using Decision Trees for control, this behavior also makes use of the ability to reason about action-execution time to eliminate options that would not have adequate time to be executed successfully. This multiagent behavior represents a bridge between low-level and high-level learning in the Layered Learning paradigm. The newly created behavior is tested empirically in game situations.

title: CMRoboBits: Creating an Intelligent AIBO Robot
abstract: CMRoboBits is a course offered at Carnegie Mellon University that introduces students to all the concepts needed to create a complete intelligent robot. In particular, the course focuses on the areas of perception, cognition, and action by using the Sony AIBO robot as the focus for the programming assignments. This course shows how an AIBO and its software resources make it possible for students to investigate and work with an unusually broad variety of AI topics within a single semester. While material presented in this article describes using AIBOs as the primary platform, the concepts presented in the course are not unique to the AIBO and can be applied on different kinds of robotic hardware.

title: Oracular Partially Observable Markov Decision Processes: A Very Special Case
abstract: We introduce the oracular partially observable Markov decision process (OPOMDP), a type of POMDP in which the world produces no observations; instead there is an "oracle," available in any state, that tells the agent its exact state for a fixed cost. The oracle may be a human or a highly accurate sensor. At each timestep the agent must choose whether to take a domain-level action or consult the oracle. This formulation comprises a factorization between information-gathering actions and domain-level actions, allowing us to characterize the value of information and to examine the problem of planning under uncertainty from a novel perspective. We propose an algorithm to capitalize on this factorization and the special structure of the OPOMDP, and we test the algorithm's performance on a new sample domain. On this new domain, we are able to solve a problem with hundreds of thousands of action-states and vastly outperform a previous state-of-the-art approximate technique