488964
Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low communication, but in which they can periodically synchronize in a full-communication setting. The two main contributions of this article are a flexible team agent structure and a method for inter-agent communication. First, the team agent structure allows agents to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defining a set of roles. Homogeneous agents can flexibly switch roles within formations, and agents can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our teamwork structure further includes pre-planning for frequently occurring situations. Second, the communication method is designed for use during the low-communication periods in PTS domains. It overcomes the obstacles to inter-agent communication in multi-agent environments with unreliable, single-channel, high-cost, low-bandwidth communication. We fully implemented both the flexible teamwork structure and the communication method in the domain of simulated robotic soccer, and conducted controlled empirical experiments to verify their effectiveness. In addition, our simulator team made it to the semi-finals of the RoboCup-97 competition, in which 29 teams participated. It achieved a total score of 67–9 over six different games, and successfully demonstrated its flexible teamwork structure and inter-agent communication.
13519699
Route Planning by Analogy
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.
11348530
SetA*: an efficient BDD-based heuristic search algorithm
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.
3514673
Using decision tree confidence factors for multi-agent control
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.
3080896
Defining and using ideal teammate and opponent agent models: a case study in robotic soccer
A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action options are most likely to help it achieve its goals. Ideally, an agent could learn a model of other agents' behavior patterns via direct observation of their past actions. However, that is only possible when agents have many repeated interactions with one another. We explore the use of agent models in an application where extensive interactions with a particular agent are not possible, namely robotic soccer. In robotic soccer tournaments, such as RoboCup (Kitano et al., 1997), a team of agents plays against another team for a single, short (typically 10-minute) period. The opponents' behaviors are usually not observable prior to this game and there are not enough interactions during the game to build a useful model. We introduce "ideal-model-based behavior outcome prediction" (IMBBOP). This technique predicts an agent's future actions in relation to the optimal behavior in its given situation, This optimal behavior is agent-independent and can therefore be computed based solely on a model of the world dynamics. IMBBOP does not assume that the other agent will act according to the theoretical optimum, but rather characterizes its expected behavior in terms of deviation from this optimum.
39948930
Real-Time Randomized Path Planning for Robot Navigation
Mobile robots often must find a trajectory to another position in their environment, subject to constraints. This is the problem of planning a path through a continuous domain Rapidly-exploring random trees (RRTs) are a recently developed representation on which fast continuous domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our planning algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve replanning efficiency and the quality of generated paths. ERRT is successfully applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more efficient for replanning than a basic RRT planner, performing competitively with or better than existing heuristic and reactive real-time path planning approaches. ERRT is a significant step forward with the potential for making path planning common on real robots, even in challenging continuous, highly dynamic domains.
52827044
CMRoboBits: Creating an Intelligent AIBO Robot
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.
45870584
A Case-Based Approach for Indoor Location
Location is an important dimension for context-awareness in ubiquitous devices. Nowadays different techniques are used alone or together to determine the position of a person or object. One aspect of the problem concerns to indoor location. Various authors propose the analysis of Radio Frequency (RF) footprints. 
 
In this paper we defend that case-based reasoning can make an important contribution for location from RF footprints. We apply an empirical dissimilarity metric for footprint retrieval and compare this approach with the results obtained with a neural network and C5.0 learning algorithms. 
 
The RF footprints are obtained from a Global System for Mobile Communications and General Packet Radio Service (GSM/GPRS) network. Signals from these networks are particularly complex when compared to the ones obtained from WiFi or Bluetooth networks.