Title: Diagnosing a team of agents: Scaling up Written by: Meir Kalech and Gal A. Kaminka Presented by: Reymes Madrazo-Rivera

Goal: To explain a way of dealing with disagreements when they occurs in a team environment. The authors came up with three different ways of detecting and diagnosing disagreements as soon as they happen in team of behavior-based agents. They were especially focused in reducing the communications and computational requirements of the diagnosis for teams composed of behavior-based agents.

Structure: Some useful concepts Introduction to the topic Previous work in this area New methods for diagnosing disagreements Experiments and conclusions

Useful concepts: Beliefs: They provide a set of data describing the state of the environment at a given moment. Behavior-based agents: System which are decomposed into different processes or behaviors, where each process is responsible of interacting with a given characteristic of the environment [1]. Behavior: It is a mechanism of controlling the actions executed by the agent once certain preconditions, represented by predicates, are fulfilled. That is, given certain precondition that behavior can be executed. When analyzing behavior-based agents, for each agent is defined a hierarchy of behaviors, which are arranged from a general one to specific behaviors A BCD EF

Useful concepts (cont.): Behavior path: Specific sequence of behaviors followed given that for each one of them their preconditions were fulfilled. For example, for Fig.1 could be defined paths (A B E), A C F) and (A D E). Team behavior: Specific behavior path that should be executed at the same time for all members of team. For example, in Robocup could be defined two team behaviors, Attack and Defend, so that, given an initial precondition (s), all members of the team know that they have to follow certain behaviors to score a goal.

Introduction: When a team is playing, it should be an agreement between all the teammates about what strategy in general to follow. That strategy is given by the team behavior. Nevertheless it could be disagreements between members of a team what is modeled by the fact that they execute different team behaviors. For example, members of a team in Robocop are in Attack mode, but some of them executed the behavior path corresponding to Defend mode for some reason. The process followed to identify disagreements consists of comparing the agents’ team behaviors and later identifying which beliefs led to that team behavior. This is performed by a dedicated agent, who perform a diagnosis task of identifying the team members’ beliefs and selecting the conflicting ones that led to the disagreement.

Previous work for diagnosing disagreements: Two methods were used before this article: Reporting: Each teammate communicate its belief to the diagnosing agent, and the last one compares each other to find contradictions. It requires polynomial times in the number of agents. Querying: It is longer process and it is the one that served as the base for the three new methods presented in this article. It has three steps:  First, all those behaviors associated to the computed actions (linear complexity in the number of behaviors) are searched by creating later a set of behavior-path hypotheses that contains behaviors associated to the observed actions.  For each one of those behavior-path hypotheses, the diagnosing agent creates a set of belief hypotheses that are related to that specific behavior path hypothesis. Belief hypotheses are all the possible values to evaluated preconditions. For example, for a precondition of (p  q), the possible hypotheses are (p  q), (p   q) and (  p  q). (exponential complexity in the number of beliefs)  With those beliefs hypotheses, the diagnosing agent can start querying the observed agent to determine if it is traversed the same behavior path that matches with the computed actions.  At the end the observer agent has the beliefs for each observed agent and compares them (polynomial complexity).

New methods for diagnosing disagreements: Three methods were proposed in this article. Each one to reduce the complexities mentioned before: 1. Behavior querying: This one avoids the long process of deducing all possible path behaviors by using communication. That is, by querying the analyzed agent about the path that it followed. In that way are avoided the rest of possible behavior path that were not used. So now, what it was a process linear complexity process in the number of behaviors becomes in a constant complexity problem O(1). In fact, also will be reduced the number of belief hypotheses to deal with, because it will be carried out just for one behavior path instead of a group of them.

New methods for diagnosing disagreements (cont.): 2. Shared belief: This one looks for reducing the exponential growth in the number of beliefs that is associated with a given behavior path. The idea now is to infer the prepositions associated to a belief, without considering their values. So, once the diagnosing agent has the prepositions associated to each agent’s preconditions, it compares those prepositions for each pair of analyzed agent to find share prepositions, which actually could be the source of contradictions. For example, consider that agents A and B consider prepositions (p,q) and (p,r) respectively. So, to determine whether A and B disagree, it will be enough to querying A and B for both values of the share preposition p (p and  p). It could be that A believes p and B believes  p, and that is the source of disagreement that caused that each one execute a different behavior path (Those values could be sensed differently because agents were in different physical locations). So far communications between agents keep increasing with the number of agents because all pair of agents are compared. But, the complexity reduce considerably (to a linear process in the number of beliefs) since inferring the exponential number of believe hypotheses is not necessary anymore.

New methods for diagnosing disagreements (cont.): 3. Grouping: The last process carry out by the diagnosing agent is the comparison between teammates’ beliefs. This is a polynomial process in the number of agents to compare and in the number of beliefs involved. This last method creates groups of agents composed of those with similar role into the team and the same behavior path. As a consequence, the diagnosing agent will select a single agent from each group to compare their beliefs, instead of comparing the belief of each observed agent. This method first uses behavior querying to get each agent behavior path. Agents groups are then built by considering that previous information and each observed agent’s role. Then the diagnosing agent select one observed agent per group and carry out for them a share believe process to finally determine possible sources of disagreements between them.

Experiment design and valuation: Those methods were evaluated on a multi-agent system (ModSAF). This application models teams of helicopter pilots. For that experiment were considered two roles for pilots and four behavior paths. The number of agents was increased from 6 to 150 with an step of eight. Four models were considered for the study in addition to the initial methods of Reporting and Querying: 1. Behavior: The diagnosing agent just uses behavior querying (so, it works with the proper behavior path). So, it completes the process by generating belief hypotheses and querying observed agents about those belief hypotheses. 2. Belief: The share belief method is used to generate belief hypotheses (so, it works with values of preconditions’ prepositions shared by agents). 3. Behavior+Belief: It’s a combination of the two first. After determining the behavior path of a given agent by using behavior querying, then possible disagreement is identified by using share belief. 4. Grouping: It is also a combination of the two first method, but this time groups of agents according to their role and behavior paths are created, and after that the share belief is applied to diagnose disagreements, but the comparison will be carried out just between the representative agents of each group.

Experiment design and valuation (cont): The experiment consisted of plotting each model in two different graphs. One of (Number of Beliefs Messages Used vs Number of Agents) and the other one of (Runtime in Milliseconds vs Number of Agents). The conclusion for that test was that the runtime grows polynomially in the number of agents for all the models due to the number of comparisons, except for the Grouping one, where the complexity is reduced to linear growth because we are dealing with a fixed number of comparisons. That reduced number of comparisons also leads to a reduction in the number of messages. The two other new method by themselves (behavior querying and share belief) do not seem to have much contribution in the reduction of runtime or the number of messages. That happened because in that experiment the authors were focused in checking the influence of the number of agents and that is why the number of beliefs were small as well as the number of behaviors.

Conclusions: It was presented a method that reduces the communication and run-time when diagnosing disagreements between teammates when a great deal of agents is involved. Firstly was reduced the number of communications initially by querying about the behavior path followed. Later on the communication was much more reduced by creating groups of agents according to their role and their computed behavior path. Consequently, the diagnosis process continues by considering a representative agent of each group.

THANKS……………