Teamwork Objectives The RoboCup Teamwork Challenge addresses issues of real-time planning, re-plannig, and execution of multi-agent teamwork in a dynamic adversarial environment. Major issues of interest in this specific challenge for the 97-99 period are architectures for real-time planning and plan execution in a team context (essential for teamwork in RoboCup). In addition, generality of the architecture for non-RoboCup applications will be an important factor. Teamwork in complex, dynamic multi-agent domains such as Soccer mandates highly flexible coordination and communication to surmount the uncertainities, e.g., dynamic changes in team's goals, team members' unexpected inability to fulfil responsibilities. Unfortunately, implemented multi-agent systems often rely on preplanned, domain-specific coordination that fails to provide such flexibility. First, it is difficult to anticipate and preplan for all possible coordination failures; particularly in scaling up to complex situations. Thus, it is not robust enough for dynamic tasks, such as soccer games. Second, given domain specificity, reusability suffers. Furthermore, planning coordination on the fly is difficult, particularly, in domains with so many possible actions and such large state spaces. Indeed, typical planners need significantly longer to find even a single valid plan. The dynamics of the domain caused by the unpredictable opponent actions make the situation considerably more difficult. A fundamental reason for these teamwork limitations is the current agent architectures. Architectures such as Soar, RAP, IRMA, and BB1 facilitate an individual agent's flexible behaviors via mechanisms such as commitments and reactive plans. However, flexible individual behaviors, even if simultaneous and coordinated, do not sum up to teamwork. A common example provided is ordinary traffic, which even though simultaneous and coordinated, is not teamwork. Indeed, theories of collaboration point to fundamentally novel mental constructs as underlying teamwork, such as team goals/plans, and joint commitments, lacking in current agent architectures. In particular, team goals and plans are not explicitly represented; furthermore, concepts of team commitments are absent. Thus, agents cannot explicitly reason about their dynamic team goals and plans; nor flexibly communicate/coordinate when unanticipated events occur. For instance, an agent cannot itself reason about its coordination responsibilities when it suddenly realizes that the team's current plan is unachievable --- e.g., that in the best interest of the team, it should inform its teammates. Instead, agents must rely on domain-specific coordination plans that address such contigencies on a case-by-case basis. The basic architectural issue in the teamwork challenge is then to construct architectures that can support planning of team activities, and more importantly execution of generated team plans. Such planning and plan execution may be accomplished via a two tiered architecture, but the entire system must operate in real-time. In RoboCup Soccer Server, sensing will be done in every 300 to 500 milli-seconds, and action command can be dispatched every 100 milli-second. Situation changes at milli-second order, thus planning, re-planning, and execution of plans must be done in real-time. Technical Issues We present a key set of issues that arise assuming our particular two tiered planning and plan-execution approach to teamwork. Of course, those who approach the problem from different perspective may have different issues, and the issues may change depending on the type of architecture employed. The following is the envisioned teamwork challenge in this domain: (i) a team deliberatively accumulates a series of plans to apply to games with different adversarial teams; (ii) each game plan is organized as a graph-structured network of different plan segments labeled with specific contingencies that should trigger the shift in plan traversal; (iii) game plans are defined at an abstract level that needs to be refined for real execution; (iv) real-time execution in a team-plan execution framework/architecture that is capable of addressing key contigencies. Such an architecture also alleviates the planning concerns by providing some ``commonsense'' teamwork behaviors --- not all of the coordination actions are required to be planned in detail as a result. The key research tasks here are: Contingency planning for multiagent adversarial game playing: Before a game starts, one would expect the team to generate a strategic plan for the game that includes contingency plan segments that are to be recognized and eventually slightly adapted in real-time. Two main challenges can be identified in this task: Definition of strategic task actions with probabilistic applicability conditions and effects. Uncertainty in the action specification is directly related to the identification of possible probabilistic disruptive or favorable external events. Definition of objectives to achieve. In this domain, the goal of winning and scoring should be decomposed in a variety of more concrete goals that serve the ultimate final scoring goal. Examples are actions and goals to achieve specific attacking or defending positioning. Plan decomposition and merge: A correspondence between team actions and goals and individual actions and goals must be set. The team plan decomposition may create individual goals that are not necessarily known to all the team players. Furthermore, within the contingency team plan, it is expected that there may be a variety of adversary-independent and adversary-dependent goals. The decomposition, coordination, and appropriate merge of individual plans to the service of the main team plan remain open challenging research tasks. RoboCup provides an excellent framework to study these issues Executing Team Plans: Team plan execution during the game is the determining factor in the performance of the team. It addresses the coordination contigencies that arise during the execution, without the need for detailed, domain-specific coordination plans. Execution also monitors the contingency conditions that are part of the global contingency team plan. Selection of the appropriate course of action is driven by the state information gathered by execution. Evaluations The Teamwork Challenge scenario described above has been idealized by several AI researchers, at least in the planning and multiagent communities. RoboCup, both in its simulated and real leagues, provides a synergistic framework to develop and/or test dynamic planning multiagent algorithms. Specifically, we are planning to evaluate the architecture and teams in the following evaluation scheme: Basic Performance: The team must be able to play reasonably well against both the best hand-coded teams, which has no planning, and against other planning-based systems. Relative performance of the team can be measured by actually playing a series of games against other unknown teams. Thus, basic performance will be measured by: Performance against hand-coded teams. Performance against other teams. Robustness: The robustness in teamwork means that the team, as a whole, can continue to carry out the mission even if unexpected changes, such as accidental removal of the players in the team, sudden change of team conposition, or changes in operation environment. For example, if one of players in the team was disabled, the team should be able to cope with such accidents, by taking over the role of disabled players, or reformulating their team strategy. Thus, this evalution represents a set of unexpected incidents during the game, such as: Some players will be disabled, or their capability will be significantly undermined by these accidents. Also, some disabled players may be enabled later in the game. Opponent switch their strategy, and the team must cope with their new strategy in real time. Some of opponent's players will be disabled, or their performance will be significantly undermined. These disabled players may come back to the game later. Teammate changes during the game. Weather factor changes. The RoboCup Teamwork Challenge therefore is to define a general set of teamwork capabilities to be integrated with agent architectures to facilitate flexible, reusable teamwork. The following then establish the general evaluation criteria: General Performace: General performance of the team, thus the underlying algorithms, can be measured by a series of games against various teams. This can be divided into two classes (1) normal compeitions where no accidental factors involved, and (2) contigency evaluaiton where accidental factors are introduced. Real-Time Operations: The real-time execution, monotoring, and replanning of the contingency plan is an important factor of the evaluaiton. For any team to be successful in the RoboCup server, it must be able to react in real time: sensory information arrives between 2 and 8 times a second and agents can act up to 10 times a second. Generality: Reuse of architecture in other applications: Illustrate the reuse of teamwork capabilities in other applications, including applications for information integration on the internet, entertainment, training, etc.? Conformity with Learning: Finally, given the premises above and the complexity of the issues, we argue and challenge that a real-time multiagent planning system needs to have the ability to be well integrated with a learning approach, i.e., it needs to refine and dynamically adapt and refine its complete behavior (individual and team) based on its past experience. Other issues such as reuse of teamwork architecture within the RoboCup community, and planning for team players that are not yet active in order to increase their probability of being useful in future moves, such as role playing and positioning of the team players that {\it do not} have the ball, will be considered, too.
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