1. © L. M. Camarinha-Matos General Aspects on Multi-Agent Systems Luis M. Camarinha-Matos Joint Workshop UvA - UNL Amsterdam. 30 Mar 98

2. © L. M. Camarinha-Matos Concept of Agent “A piece of software which performs a given task using information gleaned from its environment to act in a suitable manner so as to complete the task successfully. The software should be able to adapt itself based on changes occurring in its environment, so that a change in circumstances will still yield the intended result” G. W. Lecky-Thompson “A piece of software that acts as a representative for another party, for the express purpose of performing specific acts that are seen to be beneficial to the represented party” K. Heilmann at al. Always a representative of another party or can have its autonomous existence ?

3. © L. M. Camarinha-Matos Concept of Agent -Properties (weak definition)- Autonomy: agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state Social ability: Agents interact with other agents and (possibly) humans via some kind of agent communication language Reactivity: agents perceive their environment and respond in a timely fashion to changes that occur in it. Proactivity: agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative. Temporal continuity: agents are continuously running processes (either running active in the foreground or sleeping/passive in the background), not once-only computations or scripts that map a single input to a single output and then terminate. Goal orientedness: an agent is capable of handling complex, high-level tasks. The decision how such a task is best split up in smaller sub-tasks, and in which order and in which way these sub-tasks should be performed, should be made by the agent itself. Hermans 97

4. © L. M. Camarinha-Matos Concept of Agent -Properties (stronger definition)- Mobility: the ability of an agent to move around an electronic device. Benevolence: is the assumption that agents do not have conflicting goals, and that every agent will therefore always try to do what is asked of it. Rationality: is (crudely) the assumption that an agent will act in order to achieve its goals and will not act in such a way as to prevent its goals being achieved - at least insofar as its beliefs permit. Adaptivity: an agent should be able to adjust itself to the habits, working methods and preferences of its user. Collaboration: an agent should not unthinkingly accept (and execute) instructions, but should take into account that the human user makes mistakes (e.g. give an order that contains conflicting goals), omits important information and/or provides ambiguous information. An agent should then ask questions or even refuse to execute certain tasks that would cause damage to others. Herman 97 Agents that fit the stronger definition (from AI) can have one or more of the following characteristics:

5. © L. M. Camarinha-Matos Concept of Agent -Properties (another definition)- Autonomy: the intelligent agent must have the capability to take actions leading to the completion of some task(s) or objective(s), without trigger or impetus from end-user. There must be an element of independence to the agent. The agent is motivated to achieve its mission, therefore it seeks out opportunities to further progress towards its goals. Communication Ability: Intelligent agents must, in the course of achieving their objectives, access information from third party sources about the current “state” of the external environment. A communication language (protocol) is necessary. Capacity for cooperation: Intelligent agents must have a collaborative “spirit” in order to perform mutually-beneficial but complex tasks. Capacity for reasoning: an agent can possess the ability to infer and extrapolate based on current knowledge and experiences - in a rational, reproducible way. This is the characteristic that distinguishes an intelligent agent from a more “robotic” agent. Adaptive behavior: agents should be able to examine the external environment and the “success” of previous actions taken under similar conditions, and adapt their actions to improve the probability of successfully achieving their goals. Trustworthiness: the user must be highly confident that its agents will act and report truthfully, and will act for the user’s own good. Heilmann at al. 96

6. © L. M. Camarinha-Matos Concept of Agent -Other aspects- Some researchers try to build graphical representations of the agents (humanoid) capable of representing internal states such as “emotions”. –E.g. Robotic emotions [Fumio Hara] Microsoft Chat

7. © L. M. Camarinha-Matos MAS & Applications Multi-Agent System (MAS) - a system comprising several agents that “live” and interact in the same environment. Some Application Areas: –Advanced Manufacturing Management Systems –Agents as representatives of machines, users, business processes, etc. –Intelligent Information Search on Internet –Some agents may show learning capabilities (learn the preferences of their users, …) –Shopping Agents in Electronic Commerce –With search, price comparison, and bargaining capabilities –Surveillance Agents –For information search or to look for special events informing their users of relevant news –Assistance Agents –E.g. for diagnosing problems in the network –Etc.

8. © L. M. Camarinha-Matos KQML Knowledge Query and Manipulation Language (KQML) is a language that is designed to support interaction among intelligent software agents. It was developed by the ARPA. KQML is both a message format and a message-handling protocol to support run- time knowledge sharing among agents. KQML focuses on an extensible set of performatives, which defines the permissible “speech acts” agents may use and comprise a substrate on which to develop higher-level models of interagent interaction such as contract nets and negotiation. Example: (ask-all :content "price(IBM, [?price, ?time])" :receiver stock-server :language standard_prolog :ontology NYSE-TICKS Some preformatives: –ask-one, ask-all, ask-if, deny, evaluate, pipe, reply, tell,...

9. © L. M. Camarinha-Matos “Agentification” of legacy systems Legacy systems can be transformed into agents –Example: Robot controller -> Robot agent Server: represents the services offered by the robot controller Manager: represents the robot agent in the MAS community Agenda: keeps information on the assigned tasks, priorities, etc.

10. © L. M. Camarinha-Matos Negotiation in MAS –A way to materialize cooperation between agents when there are tasks to be performed and various potential candidates. –The cooperation is achieved through task sharing: An agent may subcontract another agent to perform a task. A task (plan) may be decomposed in a hierarchy of tasks (hierarchical planning). –How to assign tasks when there are various potential candidates? A fixed assignment structure is one possibility. Negotiation is a more flexible approach (although with higher overhead).

11. © L. M. Camarinha-Matos Negotiation in MAS... Phase 1: Task Announcement -The contractor agent publicly announces a task. -Potential candidates evaluate the task according to their won skills and availability. Phase 2: Submission of Bids / Proposals -Agents that satisfy the requiremenst, i.e., are able to perform the task, send their bid / proposal to the contractor.

12. © L. M. Camarinha-Matos Negotiation in MAS... Phase 3: Selection -The selection of the best candidate is made by the contractor based on received bids and on the CVs of the candidates. Phase 4: Contract awarding -A contract is established between the contractor and the selected candidate. -A privileged bilateral communication channel is established between the two agents.

13. © L. M. Camarinha-Matos Negotiation in Dynamic Scheduling Scheduling problem: assignment of tasks to machines / production resources. Dynamic scheduling: when the scheduling is adapted to changes in the environment - breakdown of machines, temporary unavailability of machines, changes in tasks’ priority, delays in delivery of components / raw materials, arrival of a high priority order, etc. Each manufacturing resource can be transformed into an agent [Rabelo, Camarinha-Matos 95]

14. © L. M. Camarinha-Matos Negotiation in Scheduling... Tasks can be represented by a hierarchical plan (business processes). Leaves of this hierarchy (enterprise activities) correspond to the tasks to be performed by manufacturing resources. [Rabelo, Camarinha-Matos, 95]

15. © L. M. Camarinha-Matos Negotiation in Scheduling...

16. © L. M. Camarinha-Matos Negotiation in Scheduling...  Scheduling Supervisor (SS): is the class whose (unique) instance performs the global scheduling supervision.  Enterprise Activity Agent (EAA): is the class whose instances are associated to the production resources, i.e., resources are represented by EAA. These agents are the executors of tasks.  Local Spreading Center (LSC): is the class whose instances represent functional clusters of EAAs in order to avoid announcement broadcasting and hence to make the selection of an EAA faster. They are also responsible to select the most suitable agent for a certain task after the negotiation process.  Consortium (C): is the class whose instances are dynamically created to supervise (locally and in a decentralized way) the schedule of a logical arrangement of EAAs selected to execute an entire task.

17. © L. M. Camarinha-Matos Agents in Concurrent Engineering Business Process Assistant as a representative of the engineer in the MAS community.

18. © L. M. Camarinha-Matos MAS Environments