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Published in David Burden, Maggi Savin-Baden, Virtual Humans, 2019
David Burden, Maggi Savin-Baden
In AI research, one of the most common motivation/planning models is the Belief-Desire-Intentions (BDI) model (Rao and Georgeff 1991). The AI has a set of assumptions (beliefs) about the current state of the world (and itself), a set of goals (called desires), and then a set of possible options or plans (which become intentions once chosen) about how to achieve those goals. The system then dynamically assesses the gaps between beliefs and desires, chooses the best option or plan as its intention, and then enacts that plan until either the desire is realized, or another desire has to be handled, or the plan hits a problem – when it then starts to re-plan. Agentspeak(L) is a formal language to implement a BDI system (Rahwan et al., 2004).
ALAS: agent-oriented domain-specific language for the development of intelligent distributed non-axiomatic reasoning agents
Published in Enterprise Information Systems, 2018
Dejan Sredojević, Milan Vidaković, Mirjana Ivanović
One of the first AOP languages was AgentSpeak (Rao 1996). It’s first and foremost goal was the programming of Beliefs-Desires-Intentions (BDI) agents. It is used as an abstract framework for BDI agents as well (Georgeff et al. 1999). Originally, AgentSpeak was used for understanding of the connection between BDI architectures with practical use (e.g. Procedural Reasoning System) (Georgeff and Lansky 1987) and formalization of the ideas in the background of the BDI. For this, the AgentSpeak used modal logics (Rao and Georgeff 1998). AgentSpeak agents communicate by exchanging messages. ALAS is inspired by this concept of the inter-agent communication. The main language constructs of AgentSpeak are beliefs, goals and plans. Goals can be either achievement or test. Test goals simply check the belief base and determine if some facts are correct or incorrect. To execute plans, achievement goals are used (Bordini, Hübner, and Wooldridge 2007; Mitrović 2015). ALAS is also based on language constructs. Some of the AgentSpeak concepts such as the difference between private and public services, had influence on ALAS development although it has never been put into practical use. Development of the Jason (2017) interpreter contributes to popularity of the AgentSpeak (Bordini et al. 2009; Wang 2013).
Going beyond BDI for agent-based simulation
Published in Journal of Information and Telecommunication, 2019
AOP was originally proposed by Shoham (1993) as a specialization of OOP. Shoham motivated AOP with cases in which multiple entities interacted with each other in order to manufacture cars and reserve plane tickets. In AOP, each entity (now called an agent) maintains a mental state of beliefs, capabilities and decisions that have dedicated terms with a formal syntax. Communication with other agents occurs through speech-act inspired messages. Some of the approaches to programming languages designed for AOP include: AgentSpeak(L) (Rao, 1996) in which an agent has a database of plans or rules for choosing actions that match its current mental state. The agent programming platform Jason (Bordini et al., 2007) implements AgentSpeak(L).Languages based on logic programming such as 3APL (Hindriks et al., 1999), 2APL (Dastani, 2008), and GOAL (Hindriks, 2009).Jack (Busetta et al., 1999; Winikoff, 2005) which extends Java with agent programming keywords.A combination of XML and Java. This approach is used in the agent programming platform Jadex (Pokahr, Braubach, & Lamersdorf, 2005).These programming languages use BDI as a common paradigm for a mental model but as it can be seen, they have very different approaches to implementing it. The BDI paradigm comes from philosophy and the mental model can be given formal syntax and semantics with epistemic logics. Other logics such as first-order logic and temporal logics can be used to specify world models of concepts and dynamics. Given a specification it is then possible to use logic reasoning to verify properties of the specification. Thus logic provides a theoretical framework for specifying and verifying properties of agent programs. In the programming languages AgentSpeak(L), 3APL, 2APL and GOAL, the agents also use logic to do reasoning in their decision making.