Cognition and Game Theory in the Design of a

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Cognitive Biases, Game Theory, Negotiation, Resource. Allocation ... INTRODUCTION. The focus of ... optimal solutions under conditions approximating the relevant ...... [14] Osborne, M. J. and A. Rubinstein, A COURSE IN GAME. THEORY ...
Cognition and Game Theory in the Design of a Collaboration Manager for Shared Control of Multiple UV Assets Marvin S. Cohen, Onur Sert, Melanie LeGoullon, Ewart de Visser, Amos Freedy, Gershon Weltman

Mary Cummings

Perceptronics Solutions, Inc. [email protected]

MIT Humans and Automation Laboratory [email protected]

ABSTRACT Concurrent requests for assets such as Unmanned Vehicles (UV) by distributed semi-autonomous teams pose a difficult challenge to coordination and resource optimization. A Collaboration Management Interface system was designed on the basis of a requirements analysis of the UV domain, research findings on group cognition, and concepts from game theory. The system analyzes the joint utility space for concurrent plans, matches them to appropriate game structures, selects an appropriate tactic (centered on information, problem solving, or negotiation), and implements the collaboration scheme in an interface protocol. The methods have considerable promise for coordination of distributed teams in a variety of contexts and for reducing cognitive biases associated with collaboration.

KEYWORDS: Collaboration, Decision Making, Cognitive Biases, Game Theory, Negotiation, Resource Allocation, Unmanned Vehicles, Command and Control

1. INTRODUCTION The focus of this paper is on a Collaboration Management Interface (CMI) that mediates among humans who need services of unmanned vehicles, humans who control these assets, and an Automated Mission Scheduler (AMS). The CMI utilizes novel adaptive strategies for efficient conflict resolution among distributed users with interests that only partially coincide. The strategies derive from a framework based on game theory for classifying types of interactions among potential UV users. Collaboration

protocols have been implemented in the CMI interface corresponding to different classes of interactions. As a result, CMI facilitates information sharing, problemsolving, or negotiation as needed to resolve conflicts and optimize with respect to disparate criteria and collective goals. More specifically, it permits clients, such as tactical commanders in need of information collection resources, to develop and submit requests, review automatically generated plans, evaluate alternative possibilities, and negotiate with other clients or higher-level commanders when appropriate. It also supports operator tasks of reviewing, evaluating, adjusting, and responding to multiple simultaneous client requests. In the next section, we review related work on the control and tasking of unmanned vehicles, bringing attention to a gap in support for collaboration among clients with competing requests. Then we characterize the difficulties in the UV environment caused by such competition. The gap in research and the difficulties in actual practice jointly motivate the inclusion of a Collaboration Management Interface within the AMS system. We then describe a framework for collaborative conflict resolution based on non-cooperative and cooperative game theory. Our account of the framework includes descriptions of (1) how the CMI estimates the utility to a client of a proposed response to the client’s request, (2) characteristic patterns that may appear in the joint utility space of responses to multiple clients, and (3) the approximate correspondence between those patterns and a set of paradigmatic games. Finally, we describe a collaboration schema, or interaction protocol, for each pattern that enables clients and operators to arrive at optimal solutions under conditions approximating the relevant game. We also describe some of the interface features of the CMI that implement these ideas.

In the Conclusion, we consider issues for future research. We hypothesize that this technology (1) will mitigate specific cognitive biases identified as hindrances to efficient cooperation in empirical research on group decision making and negotiation and (2) will facilitate agile adaptation of distributed teams in decentralized high tempo environments.

2. RELATED WORK Recent advances in robotics and automation have laid the foundation for increased military use of autonomous unmanned vehicles. Mixed teams of manned and unmanned forces will perform a broad variety of missions, including reconnaissance and surveillance, forward-deployed offensive operations, and tactical decoys. Teams of heterogeneous vehicles will operate autonomously in dynamic, unpredictable environments under the management of decreasing numbers of human team members [21]. These developments introduce research challenges at two levels: Low-level control of UVs and high-level tasking and resource allocation. For low-level control, some recent work focuses on autonomous cooperation among vehicles using cooperative game theory [17] or market based [26] approaches. Other work has focused on coherent visualization of the battlefield state, along with vehicle capabilities and limitations, by operators or tactical users at varying levels of detail [10]. In most cases the control modules utilize a hierarchical architecture [3] that separates the autonomous lower- level path planning and navigation from higher-level decision making, which may be either autonomous or manual. For higher level planning, some recent work has addressed design of human-machine interface protocols and displays for structuring the cooperation between vehicles and operators [7] and among geographically dispersed operators [26]. Other work has focused on methods for automated distribution and scheduling of tasks among unmanned vehicles, including genetic algorithms [18] and multi-agent algorithms [6]. As an example of the latter, AMS utilizes a Distributed Constrained Optimization Problem (DCOP) formalism to efficiently optimize shared use of multiple UV resources by multiple clients and operators [13][15]. Because DCOP supports truly distributed computation, it is not vulnerable to loss of a centralized “auctioneer,” achieves a partial solution even when some agents are inaccessible, and avoids unnecessary dissemination of sensitive information about individual users. Little attention has been payed thus far to the design and

functioning of a human-computer interface for collaboration among clients with competing demands on UV resources. Advanced approaches are necessary to increase the cognitive efficiency of human-human interactions in support of human-robot teams. Such advances are necessary to relieve a potentially significant bottleneck in the current planning process (see next section) and to ensure that DCOP generated plans take full account of client needs in the light of multi-level mission objectives, dynamic battlefield information, and shifting resource availability. CMI is a response to this problem. The Collaboration Management Interface supports DCOP-mediated collaboration among clients and between clients and operators in decisions about the optimal allocation of UV assets. More specifically, it supports client assessments of potential requests before submission, guides DCOP in the generation of proposals likely to be acceptable to clients, supports client assessments of DCOP offers, and if necessary supports a negotiation process with other clients, operators, or higher command to resolve residual conflicts.

3. CHALLENGES IN THE UV DOMAIN 3.1. The Collaboration Bottleneck Achievement of mission objectives may be compromised in many environments because of frequent conflicts among requests for UV resources. In such environments, there are significant operational delays while requests are sorted out and prioritized. Further delays occur while recommended plans are explained in order to gain user acceptance. Typical consequences of delay include depletion of fuel, loss of surprise, and plan obsolescence due to changes in facts on the ground, new information, or requirements for timely or synchronized action. Excessive use of communication bandwidth is another by-product. In addition, the resulting plans may not optimize the use of UV resources. There may be errors in the prioritization of competing requests, failure to fully utilize available resources, and missed opportunities to realize synergies by synthesizing multiple requests into a single optimal plan. Difficulties are exacerbated by uncertainty about the basic facts required to prioritize requests and develop a satisfactory plan. For example, in addition to uncertainty about enemy location, activities, and intent, there may be ignorance or uncertainty about the existence of other friendly activities in nearby or overlapping spatial

Figure 1. CMI Client Screen with Sequence of Steps for Developing UV Plan.

regions, about potential interference among these operations, and priorities among them with respect to higher objectives. There may also be uncertainty about existing UV resources, their capabilities and limitations, and windows of availability.

3.2. Collaboration Obstacles Research on collaboration suggests that some additional factors may help explain the delay, cognitive effort, and sub-optimality of results associated with conflicting UV requests. Among these obstacles are: (1) the Common Knowledge Effect, i.e., the tendency of group members not to mention uniquely held information, and errors that result when the correct decision depends on combining information from different individuals [20]. (2) Group Think, which is a socialization bias inducing conformity rather than diversity of thought [8]. (3) Illusory Conflict that results from overestimating the extremeness of the

other party’s position, assuming a fixed pie, or applying egocentric concepts of fairness [23].

3.3. Relevance of Game Theory Using game theory to analyze collaboration among friendly commanders (in need of UVs) and operators or coordinators (who manage UVs) might surprise those who associate game theory with zero-sum competition. Our study of the UV domain leaves no doubt that commanders’ requests often do compete head-to-head for scarce UV resources. However, this is not the end of the story. Game theory is useful precisely because it can help us facilitate cooperative behavior in conditions where it might not otherwise be achieved. First, game theory applies to a wide range of interactions that mix competition with opportunities for mutually beneficial coordination, generating predictions of what

rational players would do in each case [4] [16]. Even when cooperation is desired by all parties, there are often obstacles to achieving it, which game theory can clarify. Second, a branch called cooperative game theory predicts what rational players should do when the larger context of the game enables them to confer before acting and ensures that any commitments they make are binding. Such a context may be provided by legal enforcement, expectations of reciprocity, fear of loss of reputation, personal relationships, or childhood conditioning. It is not unreasonable to assume that such a context is approximated, for one or all of the above reasons, when commanders and UV operators jointly determine the allocation of UV resources with the support of the AMS. In situations where competition is dominant and negotiations tend to be time consuming, the theory suggests ways to restructure the situation to facilitate rapid acceptance of a jointly agreeable solution.

4. A COLLABORATION FRAMEWORK 4.1. Utility 4.1.1. Client Inputs Clients specify objectives or tasks, which include target types, the kind of information required, time windows for collecting it, and geographic regions. The collaboration management tool gives clients the flexibility to formulate requests at different levels of granularity and specificity. Requests for specific vehicles or sensor types is optional. While specifying their objectives, clients provide several simple evaluative assessments. Client inputs are provided by the sliders shown in the upper right section of Figure 1: (1) Target importance is the value of timely, nondegraded information of the type requested (1 = low importance, 5 = average, 10 = critical). (2) Delay cost is the proportion of the total information value lost per time interval delay after or before the requested time window (0-100%). (3) Reduced Coverage cost is the proportion of the total information value lost per unit time reduction in surveillance duration (0-100%). (4) Risk importance is the cost (negative utility) of tactically undesirable events, such as a UV route’s exposing a client’s position (0 = no cost, 10 = maximum cost) Each of these assessments serves as an importance weight for variations on the specified dimension. For example, Delay Cost and Reduced Coverage Cost capture variations of information value with time from the client's point of view. For reconnaissance missions, requested start and finish times denote the interval during which obtaining the requested information has maximal value.

Providing the information earlier than the start time provides no gain in value to the client; it could be less valuable, for example, because the enemy might place an IED after the UV left the scene but before the scheduled arrival of a friendly convoy. Later than the finish time, the information starts loosing value; for example, a convoy that did not receive IED information in time would either have to accept the risk of passing through an uncleared area or be delayed. For surveillance missions, the client wants information about the entire interval, from start to finish. The information loses value for the client if the proposed interval is shorter, and also if it is offset from the requested start time. 4.1.2. Performance Scores In response to requests, AMS proposes alternative plans, i.e., possible assignments of resources to client objectives. The CMI calculates performance scores for all offers made in response to client UV requests. The scores are generated by probability models applied to the proposed plan plus stored data of two kinds: (1) information about the physical environment (terrain, weather, locations of rivers and roads, time of day), and (2) information about UV assets (air/ground/water, current locations, speed as a function of terrain, on-board sensors, and sensor performance as a function of weather and time of day and as a function of target size and distance). This information enables the CMI to generate estimates of: (1) Expected loss of accuracy in target information due to less favorable sensor characteristics, sensor distance from target, time of day, and weather. (2) Expected loss of timeliness due to shift in the time of information collection with respect to the client’s request. (3) Expected loss of timeliness due to reduced coverage of the requested interval (in surveillance missions). (4) Expected negative utility from tactical risk. 4.1.3. Evaluation Measures The CMI generates utility measures for each proposed UV allocation plan by combining the client’s evaluative assessments with the performance scores for the offer. Four measures are generated: (1) Net Task Value is the proportion of target information value remaining after reduction due to greater distance of the sensor from the target, a lower resolution sensor, and/or a less favorable time of day or weather condition. (2) Net Timeliness is the proportion of target information value remaining after reduction due to differences between the offered time window and that requested by the client. (3) Net Risk is the negative tactical impact of the offer, for example, when the UV’s route may reveal the current location of the client to enemy in the area. (4) Aggregate Utility summarizes the merit of each proposed UV allocation

package from the client’s point of view. The Aggregate Utility of an offer is Net Task Value x Net Timeliness Net Risk. If the client has specified multiple targets with varying priorities and time constraints, overall Utility is the sum of each utility measure. The Utility of the client’s request is the client’s estimate of accurate, timely target information value, and serves as a standard of comparison for AMS/operator offers. In addition to their use as an aid in collaboration, the evaluation scores are also used to generate cost/reward functions for the DCOP multi-agent optimization, increasing the likelihood that DCOP offers match client needs as closely as possible.

4.2. Joint Utility Space CMI can explain why offers do not match requests by citing either conflict or synergy with other, concurrent requests. Figure 2 helps us categorize types of situations in terms of where they land on this continuum. It graphs the joint utility space for two clients who have made concurrent requests for UV assets. For convenience, we set the Utility of each client’s request at 100, and the Utility of the status quo, i.e., no information, for each client at 0. (Origin and units may be set arbitrarily and independently for each scale. Recognition of patterns in the joint utility space does not require definition of a joint utility function that weights their relative importance. However, we are assuming that the two requests are equally feasible taken by themselves.) The point labeled A represents the plan resulting from allocation of all requested resources to Client A and none to Client B, while the point labeled B is the reverse, assuming that neither client receives any value from the activity of the other. The diagonal connecting these two points represents the set of operating points that results from shifting resources from one client to the other, given two additional assumptions: The two sets of objectives require exactly the same resources to be fully satisfied (e.g., each request needs the same UAV from 1100 to 1400 on the same day), and the Aggregate Utility that would be directly realized by the relevant client is a decreasing linear function of the amount of resources allocated to the client’s objectives. Other regions of the graph represent other possible relationships between the two requests. For example, the (100,100) point, labeled Compatible, means that the full value of each request can be realized even though the plans are carried out concurrently. This can happen for several reasons: (1) The clients’ tasks make no

Figure 2. Regions in the Joint Utility Space Represent Different Degrees of Conflict Versus Mutual Support. overlapping demands on resources; e.g., they need different UV’s, or the same UV at different times. (2) The objectives of the two plans are precisely the same; e.g., both clients request information about the same target at the same time and place. The bowed line labeled Less Competition is an intermediate case, in which the tasks require some of the same resources but not all (e.g., Client A wants the UAV from 1100 to 1230, while Client B wants it in a nearby location from 1200 to 1400), or in which they share some target information objectives but not others. Points in this region might also occur because of non-linear relationships between resources and utility for one or both of the tasks, e.g., small initial allocations of assets have large effects, while subsequent allocations have decreasing marginal influence on success. Operating points significantly above the Same Resources diagonal tend to favor concurrent operations, because on a wide range of assumptions about the relative weights of the two utility scales, they deliver more utility than could be achieved by allocating all the resources to either one of the clients alone. Proceeding in the other direction, the (0,0) point, labeled Incompatible, means that neither client realizes any benefit if resources are allocated to both. For example, the requested targets may be so far apart that travel time from one to the other would consume the entire observation period, or the route of a UV responding to one request may alert the targets of another UV request. The bowed line labeled Mutual Interference is an intermediate

condition, in which the costs of attempting both tasks (e.g., the transition time from one region to another, or the chance of alerting other targets) reduce but do not wipe out the benefits that could have been received from the resources in question. Points in this region might also occur because of non-linear relationships between resources and utility in one or both of the tasks, e.g., successful performance has an all-or-nothing quality, hence, allocations of assets have increasing marginal effect. Operating points significantly below the Same Resources diagonal) tend to disfavor concurrent operations, since on a wide range of assumptions about the relative weights of the two utility scales, they deliver less utility than could have been achieved by allocating all the resources to just one of the tasks. Finally, concurrent allocation of resources may have effects on tactical objectives above the level of the information collection tasks. A Tactical Deficit might occur, for example, if a UV responding to one client’s request endangers the other client by drawing enemy attention along its route. The result may deduct from the utility of both tasks even if they are completely compatible; the location of the label in Figure 2 is the extreme case (below the (0,0) point) in which utility is lower than would have been realized simply by not attempting either task. Conversely, a Tactical Surplus might occur if both UVs divert the enemy from the other Client’s location, or confuse the enemy about the significance of other operations by friendly units. The result may offset losses of utility due to interference (though not incompatibility); Figure 2 shows the extreme case (above the (100,100) point) which realizes more utility than the sum of the two tasks performed individually. One limitation of the classification so far is the omission of asymmetric effects, e.g., when Client A benefits but Client B is harmed or not helped by the concurrency of the other’s task. Note that this is a structural asymmetry inherent in the objectives and the situation, and is not the same as simply allocating more resources to A than to B. One client may easily share resources due to a negatively accelerated utility-resource function, while the other client cannot afford to lose any requested assets. One client may receive a tactical surplus (or deficit) from the other’s actions while the other experiences the opposite result. Asymmetric cases probably present the greatest challenge to user acceptance of resource allocation decisions, due to the appearance of unfairness. They may be offset to some degree if activities share tasks or objectives. Deeper insight into the handling of such cases is one goal of the framework described in the following sections.

4.3. Games The DCOP Mission Scheduler offers several alternative plans in response to each client’s task objectives. As shown at the bottom of Figure 1, the client reviews the proposed plans and has the option of requesting the operator’s final approval for one of them or submitting modified task objectives. In the meantime, other clients may have submitted objectives, requested plans, or received plan approvals that interact with the client’s task in any of the ways discussed above (Figure 2). (In the following, we will use “request” more generally to refer both to submitting task objectives and to asking the operator to approve a specific proposed plan.) The aim of the Collaboration Management Interface is to improve the efficiency with which clients converge on acceptable solutions. Effective explanations of denied or modified requests depend on a correct understanding of the obstacles to acceptance. These obstacles include such phenomena as the fixed pie illusion, exaggerated perceptions of the extremeness of the other party’s demands, unshared information, lack of trust in the competence or fairness of the automation or of other parties, and groupthink (which may be shared even by participants that don’t know or trust one another). Game theory provides a set of general tools for the analysis of joint decisions, as well as a set of paradigmatic games (e.g., Battle of the Sexes, Stag Hunt, Chicken, and Prisoner’s Dilemma, as shown in Figure 3) that differ in the balance of cooperative and conflictual opportunities. Each of the games graphed in Figure 3 captures a distinctive qualitative structure of joint payoff contingencies, hence, demands a somewhat different approach to jointly optimal collaborative solutions. These games and others can serve as standard (simplified) patterns that are matched to real UV resource allocation problems, based on the similarity of their joint utility graphs. The analysis of the game from both a noncooperative and cooperative point of view may then be used to formulate explanations of AMS/operator responses to clients’ requests. Such explanations are tailored to the specific obstacles most likely to block shared understanding, information pooling, or mutual trust in the situation at hand. In the game graphs of Figure 3, Client A has two plan options, A1 and A2, and Client B has two plan options, B1 and B2. Labels (e.g., A1B2) stand for combinations of plan choices by the two clients. Lines connect combinations that are only one step removed, i.e, that are reachable from one another by a change in the decision of

one participant. Dotted lines are changes under B’s control (e.g., the shift form A1B1 to A1B2 involves a choice by B); solid lines are changes under A’s control. Arrows represent the direction of change preferred by the client in control of that change. Shaded nodes are stable combinations because they have no arrows pointing away from them: Once in such a state, neither client alone can obtain more individual utility by switching to another plan. These combinations, which represent each player’s best response to the chosen response of the other, are Nash equilibria, and are identified by game theory as rational solutions for players pursuing their own interests in non-cooperative contexts. Recall that in such contexts, there is no enforcement mechanism that enables players to reach binding agreements to jointly choose a particular combination of plans. By contrast, heavily outlined rectangles are Pareto efficient and designate collectively rational solutions in cooperative contexts [6]. There is no other combination of plans that is better for one party while not harming the other. In terms of the graphs, no other nodes, and no points on a straight line connecting two other nodes, are either above or to the right (i.e., better for at least one participant), and neither below nor to the left (i.e, not worse for any participant), with respect to the node in question. Points on a straight line connecting two Pareto efficient nodes are probabilistic mixes of the plans associated with the two nodes and are also Pareto efficient. As Figure 3 shows, more than one Pareto efficient solution is usually available. A collective utility function, which weights the individual client utility scales in relation to one another, can justify a unique “best” choice of either a single node or a probabilistic mix. DCOP recommended plans should be Pareto efficient combinations of plans by all clients and should maximize utility to the organization as a whole, subject to computational limitations on the multi-agent optimization process.

(1) To the extent that the clients’ respective utilities are positively correlated across available plan combinations, especially on the Pareto frontier, their objectives are congruent and agreement should be relatively easy. To the extent that clients’ utilities are negatively correlated across available plans, especially on the Pareto frontier, their objectives are in conflict. The games in Figure 3 progress from a slope of plus 1 in the highly congruent Pure Coordination game to a slope of minus 1 in the highly conflictual Zero-Sum game. Battle of the Sexes, Prisoner’s Dilemma, and Chicken are negatively correlated on the Pareto frontier; Stag Hunt is not. (2) If Nash equilibria do not correspond to Pareto efficiency, client goals may lead them to resist DCOP offers that serve the interests of the organization as a whole. The Stag game in Figure 3 is vulnerable to this danger, but the Prisoner’s Dilemma is the most extreme case, where the unique Nash equilibrium is the only combination of plans that is not Pareto efficient. At the other extreme, in the Pure Coordination game and Battle of the Sexes, Nash equilibria correspond exactly to Pareto efficient points. Chicken and Zero Sum are intermediate. In explaining DCOP recommendations, CMI integrates problem-solving, negotiation, and informational tactics. The appropriate mix is based on the above classification: 1. Problem solving: If utilities for clients’ preferred plans are negatively correlated, CMI explains how the size of the pie can be expanded by the DCOP recommended plans. Trust in automation must be developed over time [9]. 2. Negotiation: If clients’ preferred plans reflect a Pareto inefficient Nash equilibrium, CMI explains conflicting requests and supports a process of negotiation to develop a collaborative solution. Trust in the reliability of other clients is a crucial factor [11].

5. COLLABORATION SCHEMAS 5.1. Basic Tactics The concern of the CMI is not deriving solutions, but explaining them to clients and eliciting rapid acceptance. In generating such explanations, key factors are the degree and type of conflict, if any, embodied in the underlying game and the difference between cooperative and non-cooperative solutions. Two aspects of the joint utility graphs are relevant:

3. Information. Client’s choices may be suboptimal even though neither of the above is the case, e.g., because they have chosen different Pareto efficient Nash equilibria. In this case, CMI provides information regarding other client’s choices and recommends the minimal adjustment necessary to coordinate them. The games in Figure 3 thus involve different projected interactions among clients and the collaboration interface. These interactions are managed through CMI Client screens as shown in Figure 1. (CMI also provides screens for operators, which are not shown.)

Figure 3. Illustrative Joint Utility Functions for Six Paradigmatic Games.

5.2. Application of Tactics to Games 5.2.1. Pure Coordination Game In this game, clients value the objectives of the other client to the same degree as their own, e.g., because they are mutually supporting components of a single operation. Therefore both participants need to coordinate to succeed: They each prefer A1B1 or A2B2 over A1B2 or A2B1. A “conflict” situation is one in which the participants’ independent choices happen to differ, landing them in one of the less preferred combinations (A2B1 or A1B2). A change by either one of the participants (but not both) results in a jointly preferred combination (A1B1 or A2B2) that maximizes payoffs to each. The CMI script for this game has two parts, one proactive and the other reactive. The proactive component provides timely, shared situation awareness (e.g., involving other client requests or on-going missions) to head off potential

conflict before a client submits task objectives to DCOP. This is an information tactic, shown as steps 1 and 2 in Figure 1. The reactive component provides clients with an option to submit preliminary task objectives for system vetting before submitting them as a request (information tactic, step 3 of Figure 1). 5.2.2. Battle of the Sexes In this game, payoffs are less than perfectly correlated but both parties benefit from cooperation. As in the pure coordination game, inadvertent conflict occurs if independent choices result in a jointly less preferred combination (A2B1 or A1B2). Again, a change by either participant improves the outcome for both. The distinctive feature is that the participants benefit differentially from different possible changes: Each receives significantly more utility if the other party changes (i.e., participant A prefers A1B1, while participant B prefers A2B2).

A danger in this type of game is that norms or habits will become entrenched that consistently favor one party over the other, e.g., enforcing inequality based on rank or military branch rather than contribution to overall objectives. Cases of this sort raise the issue of trust in automation: Participants need to be assured that conflicts are resolved reasonably and fairly, in a way that gives their objectives proper weight. Resolution requires not only information about others’ choices. It must include a rationale for changing one plan rather than the other (problem solving tactic, steps 4, 5, 6, and 7 in Figure 1). This rationale might involve factors outside the purview of the participants’ own objectives, e.g., impact on other operations, contribution to the larger mission, or cost and feasibility. In the absence of such considerations, it might involve reciprocity over time or even a fair but random choice. The tendency of clients to trust and accept solutions recommended by system facilitates rapid resolution. 5.2.3. Stag Hunt This game introduces the problem of trust among participants [19]. On the one hand, both clients achieve the most if they cooperate (A1/B1). For example, each party agrees to allocate some of its UV time to performing tasks needed by the other party (analogous to teaming up to hunt big game, hence “stag”). On the other hand, each party also has a viable albeit less effective alternative, analogous to hunting smaller game alone. The worst case for a particular participant is the situation in which that participant tries to cooperate (e.g., by agreeing to a plan that depends for its success on the other party), but the other party does not deliver (corresponding to A1B2 for participant A; A2B1 for participant B). As a result, both parties have incentives to take the noncooperative alternative if they suspect the other party is unreliable. When these assumptions apply, resolution of conflict requires (1) shared information about the existence and advantages to both parties of a synergistic option (information plus problem solving tactic, steps 4, 5, 6, and 7 in Figure 1), and (2) assurances of reliable implementation of cooperative plans (negotiation tactic). In some cases, the latter may require direct communication among participants (step 8 in Figure 1) 5.2.4. Prisoner’s Dilemma In this game neither party can optimize mission success by means of cooperation. The outcome most preferred by one party (A2B1 by A; A1B2 by B) is the outcome least preferred by the other. Both parties have an incentive to reject the cooperative solution (A1B1) whether or not

they regard the other party as reliable. For example, if Client B chooses B1, Client A benefits more from option A2 than from option A1; if B chooses B2, A also benefits more from A2 than from A1. An effective Prisoner’s Dilemma might occur when participants harbor fundamentally different assumptions about the situation or about the tactics most likely to succeed under the given circumstances. Resolution may require in-depth discussion and negotiation, culminating in a decision enforced by higher headquarters. (negotiation tactic, step 8). There is reason to believe that norms regarding such compromises will evolve in the CMI collaboration management context, and be sustained over generations of users [16]. A variety of empirical results support this expectation [1] [4] [22] [25].

6. CONCLUSION The concept that emerged from this work involves highly adaptive support for a graded series of conflict resolution strategies, with transitions from rapid and less costly procedures to more time-intensive ones if and only if the severity and importance of the conflict demand it. This top-level conceptualization of the UV resource allocation process will be subjected to extensive testing in future phases of our work. If proven effective, it is clearly applicable to a much wider set of organizational, team and group decision making and bargaining processes. Teams and organizations grow in size in order to accomplish a larger number of tasks and undertake larger or more complex activities. The communication and coordination overhead introduced by size is traditionally managed by departmentalization of functions, hierarchical control, and standardization of procedures, equipment, and training. A seemingly inevitable consequence is reduced agility in a dynamic external environment, such as the battlefield Some organizations, including parts of the military, have attempted to mitigate these effects by delegating more decision making authority. By avoiding central bottlenecks, small units and individual soldiers can respond more quickly to locally developed information, take advantage of opportunities that might otherwise be lost, and test innovative tactics. Distributed initiatives pose a risk when task execution must be synchronized or is otherwise tightly coupled across units. Independently executable operations jeopardize one another through inadvertent interference, lack of assumed mutual support, or competition for the same organizational resources (such as UVs). An objective of the Collaboration Management Interface is to

smoothly and rapidly coordinate distributed operations that depend on common resources without sacrificing agility and without the overhead of centralized control. Organizations and teams may achieve higher levels of performance if they can dynamically adjust their location on a continuum between uncoordinated and coordinated decision making in response to the need, such as operation in the same region, divergence of objectives, and overlapping resource requirements. The Collaboration Management Interface provides the flexibility to vary control strategies on a case by case basis when different combinations of tasks have different interdependence and agility characteristics. Collaboration among distributed units can become a bottleneck because of failure to share information (Common Knowledge Effect), a narrow perspective on the situation (Group Think), or exaggerated competition (Illusory Conflict). CMI detects situations in which each obstacle is likely to appear and uses a collaboration protocol (in effect, a persuasion strategy) designed specifically to overcome it. These strategies include information necessary for coordination, identification of opportunities for mutually advantageous synergies, more accurate perception of another unit’s needs, a better picture of how operations relate to higher level missions, fairness over time, and assurance of reliability through enforcement of commitments. Our hypothesis, to be tested in future work, is that CMI can efficiently resolve conflicts by using game theoretic tools to accurately identify and effectively address such cognitive factors.

ACKNOWLEDGMENTS The AMS project was conducted by Perceptronics Solutions, Inc. with the USC Computer Science Department, Charles River Analytics, Inc., and the MIT Humans and Automation Laboratory under OSD/ONR SBIR Phase I Contract N00014-07-M-0280. We are grateful to Marc Steinberg of ONR for his essential guidance. We also thank Milind Tambe and Jonathan Pearce of USC and Karen Harper of Charles River Analytics for their essential contributions to AMS development.

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