System Objective

A System Objective is an Intended Future State that a Goal-Directed Entity (person, group, or system) seeks to achieve through purposeful action.

• AKA: System Goal.
• Context:
  • Goal Input: Current State, Desired State, Action Capability
    • Optional Input: Resource Constraints, Environmental Conditions
  • Goal Output: Action Plan, State Change, Achievement Measure
  • Goal Performance Measure: Completion Status, Progress Level, Success Rate
  • ...
  • It can be interpreted and programmed into a AOP System by an AOP Agent Interpreter.
  • It can be a crucial component in a BDI Agent System architecture.
  • It can guide Action Selection through Decision Processes.
  • It can structure Behavior Patterns through Goal Priority.
  • It can direct Resource Allocation through Objective Alignment.
  • ...
  • It can often require Progress Monitoring through Status Tracking.
  • It can often need Plan Adaptation through Feedback Processes.
  • It can often involve Resource Management through Allocation Strategy.
  • It can often demand Performance Assessment through Success Metrics.
  • ...
  • It can range from being a Short-Term Goal to being a Long-Term Goal, depending on its Time Horizon.
  • It can range from being an Individual Goal to being a Group Goal, depending on its Entity Scope.
  • It can range from being a Simple System Goal to being a Complex System Goal, depending on its Complexity Level.
  • It can range from being a Formal Goal Specification to being an Informal Goal Description, depending on its Definition Type.
  • It can range from being an Explicit Goal Statement to being an Implicit Goal Pattern, depending on its Expression Mode.
  • ...
  • It can be instantiated by a Goal-Directed Entity through Objective Setting.
  • It can integrate with Planning Systems for Action Coordination.
  • It can connect to Control Mechanisms for Progress Regulation.
  • It can support Performance Systems for Achievement Tracking.
  • ...
• Examples:
  • Goal Types, such as:
    • Basic Goal Patterns, such as:
      • Achieve Goal for State Attainment.
      • Maintain Goal for State Preservation.
      • Cease Goal for State Termination.
      • Avoid Goal for State Prevention.
    • Advanced Goal Patterns, such as:
      • Optimize Goal for State Enhancement.
      • Test Goal for State Verification.
      • Query Goal for State Investigation.
      • Perform Goal for Action Execution.
  • Entity Goals, such as:
    • Agent Goal Types, such as:
      • AI System Goal for Artificial Agent.
      • AGI Goal Pattern for General Intelligence.
      • ASI Goal Structure for Superintelligent System.
    • Human Goal Types, such as:
      • Personal Goal for Individual Achievement.
      • Group Goal for Collective Achievement.
  • ...
• Counter-Examples:
  • Agent Intention, which represents Action Commitment rather than Desired State.
  • Agent Belief, which reflects State Understanding rather than State Objective.
  • System State, which describes Current Condition rather than Target Condition.
  • Random Outcome, which lacks Purposeful Direction towards a Specific State.
  • Emergent Pattern, which arises without Intentional Design or Goal Direction.
• See: BDI Agent System, Goal-Oriented Agent, Multi-Agent System, Utility Function, BOID Agent System, Goal Management, Objective Framework.

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References

2019a

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Belief–desire–intention_software_model Retrieved:2019-8-10.
  • The belief–desire–intention software model (BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan (from a plan library or an external planner application) from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans (choosing what to do) and executing those plans (doing it). A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer.

2019b

• (Wikipedia, 2019) ⇒ https://www.wikiwand.com/en/Belief%E2%80%93desire%E2%80%93intention_software_model#/BDI_agents Retrieved:2019-8-10.
  • A BDI agent is a particular type of bounded rational software agent, imbued with particular mental attitudes, viz: Beliefs, Desires and Intentions (BDI) (...)

    This section defines the idealized architectural components of a BDI system.

    • Beliefs: Beliefs represent the informational state of the agent, in other words its beliefs about the world (including itself and other agents). Beliefs can also include inference rules, allowing forward chaining to lead to new beliefs. Using the term belief rather than knowledge recognizes that what an agent believes may not necessarily be true (and in fact may change in the future).
      • Beliefset: Beliefs are stored in a database (sometimes called a belief base or a belief set), although that is an implementation decision.
    • Desires: Desires represent the motivational state of the agent. They represent objectives or situations that the agent would like to accomplish or bring about. Examples of desires might be: find the best price, go to the party or become rich.
      • Goals: A goal is a desire that has been adopted for active pursuit by the agent. Usage of the term goals adds the further restriction that the set of active desires must be consistent. For example, one should not have concurrent goals to go to a party and to stay at home – even though they could both be desirable.
    • Intentions: Intentions represent the deliberative state of the agent – what the agent has chosen to do. Intentions are desires to which the agent has to some extent committed. In implemented systems, this means the agent has begun executing a plan.
      • Plans: Plans are sequences of actions (recipes or knowledge areas) that an agent can perform to achieve one or more of its intentions. Plans may include other plans: my plan to go for a drive may include a plan to find my car keys. This reflects that in Bratman's model, plans are initially only partially conceived, with details being filled in as they progress.
    • Events: These are triggers for reactive activity by the agent. An event may update beliefs, trigger plans or modify goals. Events may be generated externally and received by sensors or integrated systems. Additionally, events may be generated internally to trigger decoupled updates or plans of activity.

BDI was also extended with an obligations component, giving rise to the BOID agent architecture to incorporate obligations, norms and commitments of agents that act within a social environment.

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/goal Retrieved:2017-5-31.
  • A goal is a desired result or possible outcome that a person or a system envisions, plans and commits to achieve: a personal or organizational desired end-point in some sort of assumed development. Many people or organizations endeavor to reach goals within a finite time by setting deadlines.

    It is roughly similar to purpose or aim, the anticipated result which guides reaction, or an end, which is an object, either a physical object or an abstract object, that has intrinsic value.

2011

• (Ozturk et al., 2011) ⇒ Celal Ozturk, Dervis Karaboga, and Beyza Gorkemli. (2011). “Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm." Sensors 11, no. 6
  • QUOTE: … fitness value (nectar) of the solution. In ABC model, artificial bee colonies where the goal of the bees is to find the best solution [ 28 ] are formed of three groups of bees: worker bees, onlookers and scouts. A bee waiting on the dance area to determine to choose a food source is an onlooker and a bee goes to the food source visited by it previously is a worker bee. …

2006

• (Cheong et al., 2006) ⇒ Christopher Cheong, and Michael Winikoff. (2006). “Hermes: Designing Goal-oriented Agent Interactions." In Agent-Oriented Software Engineering VI, pp. 16-27. Springer Berlin Heidelberg,
  • QUOTE: … We have presented Hermes, a goal-oriented agent interaction methodology that includes a design process, failure recovery mechanisms and a mapping from design artefacts to an executable implementation. …

2005

• (Pokahr et al., 2005) ⇒ Alexander Pokahr, Lars Braubach, and Winfried Lamersdorf. (2005). “A BDI Architecture for Goal Deliberation.” In: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems. ISBN:1-59593-093-0 doi:10.1145/1082473.1082740

2004

• (Braubach et al., 2004) ⇒ Lars Braubach, Alexander Pokahr, Daniel Moldt, and Winfried Lamersdorf. (2004). “Goal Representation for BDI Agent Systems.” In: Proceedings of the Second International Conference on Programming Multi-Agent Systems. ISBN:3-540-24559-6, 978-3-540-24559-9 doi:10.1007/978-3-540-32260-3_3

1997

• (Doran et al., 1997) ⇒ Jim Doran E., S. R. J. N. Franklin, Nicholas R. Jennings, and Timothy J. Norman. (1997). “On Cooperation in Multi-agent Systems." The Knowledge Engineering Review 12, no. 03
  • QUOTE: … Norman argues that his position does not preclude the possibility of engineering a goal-oriented agent system in which the agents are designed to coordinate their activities in the pursuit of implicit goals in an effective manner. …----

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