Collectively Cognitive Agents in Cooperative Teams Jacek Brzeziński, Piotr Dunin-Kęplicz Institute of Computer Science, Polish Academy of Sciences Barbara Dunin-Kęplicz Institute of Computer Science, Polish Academy of Sciences Institute of Informatics, Warsaw University

Goals Simulator –Collective Commitment Theory (B. Dunin-Kęplicz, Verbrugge) –Theory of Trust (Castelfranchi, Falcone) Research of some BDI systems properties when different definitions of collective commitments are used: –are agents willing to cooperate? –does the system evolve? –quality of teamwork? –how much information is needed for agents to cooperate productively? –how to calculate the degree of trust?

Overview Collective Commitments theory Simulator: –agents –interaction scenario –different CC models w.r.t. trust Test results Future work

Collective commitments Tuning machine allows to select collective commitment type w.r.t.: –group awareness about social plan –group awareness about distribution of bilateral commitments –existence of collective intention Different models of organizations

Examples of collective commitment Robust collective commitment Weak collective commitment Distributed commitment

Roles of agents Managers –generate tasks (sets of actions) to be performed by Workers for a specific salary –do not execute actions –delegate actions (or sets of actions) to other agents (Workers) Workers –perform actions –cannot delegate actions

Properties of agents Beliefs – believes(A, f) - agent A believes f Intentions – intends(A, a) - agent A intends to perform action a Abilities – able(A, a, n) - agent A is able to perform action a (n - probability of success) Trust – trusts(A, B, a, n) - agent A trusts in agent B to do a in degree of n – trust values base on direct experience. – Workers & Manager trust in Workers to execute specific actions. – Workers trust in Manager to do proper team selection.

Four levels of CPS Manager has a task (set of actions) to perform: 1. Potential recognition Manager recognizes potential for cooperative action in order to complete the task 2. Team formation Manager attempts to establish group of agents that can collectively fulfil the goal (collective intention) 3. Plan generation Social plan of achieving the goal is built and the collective commitment is formed 4. Team action Agents involved do their tasks and eventually achieve the main goal

Potential recognition M sends CFP to all the Workers in the system (content = (O - set of actions)) Workers respond with their bids M chooses the best group G that can perform the task collectively w.r.t.: –trust in Workers in the group –prices proposed by Workers –risk factor describing preferences of M

Team formation & plan generation 1 M broadcasts relevant information to all Workers in G: –an offer to form a group –an offer to participate in task –an allocation of actions –division of task O into subtasks –an offer to perform action(s) for price x

Team formation & plan generation 2 Agents decide whether to join the group Task result estimation function takes into account following factors: trust in the manager M trust in other members of the group ability to perform delegated actions If all Workers agree, then collective commitment is constructed.

Team action Workers perform actions and tell M about results If all actions have succeeded, the task also succeeds and all Workers collect their payments Results of actions are propagated to group members, depending on team awareness level Agents modify their degrees of trust in other group members (basing on direct experience)

Robust collective commitment Information sent to Workers –an offer to create a group G with intention to realize task O –an allocation of sets of actions –an offer to perform a set of actions for a price of x Task result estimation function Result propagation –All the information about successes and failures

Weak collective commitment Information sent to Workers –an offer to create a group G with intention to realize task O –a division of task O into subtasks S –an offer to perform a set of actions for a price of x Task result estimation function Result propagation –only failure information: agent, failed action, subset of O delegated to the agent

Distributed commitment Information sent to Workers –an offer to perform a set of actions for a price of x Task result estimation function Result propagation –only the team action result

Tests Configuration 16 Workers, 1 Manager 100,000 tasks 4 actions in every task abilities set randomly between 0.5 and 1 starting trust value = 1 Parameters number of successful/failed contracts efficiency difference between trust values and real abilities

Test Results Strong models of CC: –efficient –moderate number of contracts –agents learning from experience –costly (comm. resources) Weak models –unstable –many contracts, mostly failing

Robust Collective Commitment

Weak Collective Commitment

Distributed Commitment

Future work Complex organization model –more managers –more detailed trust model –more complex commitment structure –institutional restrictions Reconfiguration –role of trust Dynamic commitment model evolution