Person/Group/System Goal

A Person/Group/System Goal is an future state that a person/group/system intended to achieve.

• AKA: Objective.
• Context:
  • 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 range from being a Short-Term Goal to being a Long-Term Goal.
  • It can range from being a Individual Organism's Goal (e.g. person's goal) to being a Group Goal (organizational goal).
  • It can range from being a [[..]] to being a System Goal (e.g. software agent goal).
  • It can range from being an Fomally-Specified Person/Group Goal to being a Informal Person/Group Goal.
  • It can range from being an Explicit Person/Group Goal to being an Implicity Person/Group Goal.
  • ...
  • It can be instantiated by a Goal-Directed Person/Group/System.
  • …
• Example(s):
  • an Achieve Goal,
  • a Maintain Goal,
  • an Cease Goal,
  • an Avoid Goal,
  • an Optimise Goal,
  • a Test Goal,
  • a Query Goal,
  • a Perform Goal,
  • a Preserve Goal.
  • a Personal Goal.
  • a Game Win Goal.
  • ...
• Counter-Example(s):
  • a Agent Intention,
  • a Agent Belief,
  • an Organizational Goal,
• See: Belief-Desire-Intention (BDI) Agent System, Goal-Oriented Agent System, Multi-Agent System, Reverse Engineered Utility Function, Belief-Desire-Intention (BDI) Agent System, Belief-Obligation-Intention-Desire (BOID) Agent System.

---

---

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. …----