Planning experiments in the DALI logic programming language

Abstract—In this paper we discuss how some features of the new logic programming language DALI for agents and multiagent systems are suitable to programming agents equipped with planning capabilities. We discuss the design and implementation of an agent capable to perform STRIPS-like planning, and we propose a small but significant example. In particular, a DALI agent, which is capable of complex proactive behavior, can build step-by-step her plan by proactively checking for goals and possible actions.

DALI is a logic programming agent-oriented language defined in [1,2,3,4], fully formalized in [5,6]. DALI is fully implemented, and has been used in practice in a variety of applications [7,8,9,10]. A stable release of the DALI interpreter is publicly available at [11]. For the definition of DALI we have built under many respects upon our past work about meta-reasoning and reflection in logic programming languages [12,13,14,15,16]. In this work in particular, issues related to meta-level representation of predicates, atoms and rules are discussed in depth.

Lecture Notes in Computer Science, 2004

We acknowledge support by the Information Society Technologies programme of the European Commission, Future and Emerging Technologies under the IST-2001-37004 WASP project.

2008

Abstract Many interesting architectures for defining intelligent agents have been proposed in the last years. Logic-based architectures have proved effective for reproducing “intelligent” behavior while staying within a rigorous formal setting. In this paper, we present the DALI multi-agent architecture, a logic framework for defining intelligent agents and multiagent systems.

Woa, 2005

In this paper we introduce a form of cooperation among agents based on exchanging sets of rules. In principle, the approach extends to agent societies a feature which is proper of human societies, i.e., the cultural transmission of abilities. However, acquiring knowledge from untrustworthy agents should be avoided, and the new knowledge should be evaluated according to its usefulness. After discussing the general principles of our approach, we present a prototypical implementation. gered as if it were an external one. A DALI agent is able to build a plan in order to reach an objective, by using internal events of a particular kind, called planning goals. Actions are the agent's way of affecting the environment, possibly in reaction to either an external or internal event. An action in DALI can be also a message sent by an agent to another one. Definition 4 (Action): An action is syntactically indicated by postfix A: Action ::= << Atom A >> |message A << Atom, Atom >> Actions take place in the body of rules. If an action has preconditions, they are defined by action rules, emphasized by a new token: Definition 5 (Action rule): An action rule has the form: Action :< P reconditions. Similarly to external and internal events, actions are recorded as past actions. Past events represent the agent's "memory", that makes it capable to perform future activities while having experience of previous events, and of its own previous conclusions. Past events are kept for a certain default amount of time, that can be modified by the user through a suitable directive in the initialization file. A past event is syntactically indicated by the postfix P. Procedurally, DALI is based on an Extended Resolution Procedure that interleaves different activities, and can be tuned by the user via directives. The operational semantics of DALI is based on Dialogue Games Theory [4] [20]: the DALI Interpreter is modeled as a set of cooperating players. By means of this approach one is able to prove formal properies of the language in the form of properties that the game will necessarily fulfil. A. DALI Communication Architecture The DALI communication architecture consists of four levels. The first and last levels implement the DALI/FIPA communication protocol and a filter on communication, i.e. a set of rules that decide whether or not receive (told check level) or send a message (tell check level). The DALI communication filter is specified by means of meta-level rules defining the distinguished predicates tell and told. Whenever a message is received, with content part primitive(Content,Sender) the DALI interpreter automatically looks for a corresponding told rule. If such a rule is found, the interpreter attempts to prove told(Sender, primitive(Content)). If this goal succeeds, then the message is accepted, and primitive(Content)) is added to the set of the external events incoming into the receiver agent. Otherwise, the message is discarded. Symmetrically, the messages that an agent sends are subjected to a check via tell rules. The second level includes a metareasoning layer, that tries to understand message contents, possibly based on ontologies and/or on forms of commonsense reasoning. The third level consists of the DALI interpreter.

Intelligenza Artificiale, 2011

IGI Global eBooks, 2011

This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation & Reasoning, action languages like K allow to formalize complex planning problems involving non-determinism and incomplete knowledge in a very flexible manner. By giving an overview of existing planning languages and comparing these against our language, we aim on further promoting the applicability and usefulness of high-level action languages in the area of planning. As opposed to previously existing languages for modeling actions and change, K adopts a logic programming view where fluents representing the epistemic state of an agent might be true, false or undefined in each state. We will show that this view of knowledge states can be fruitfully applied to several well-known planning domains from the literature as well as novel planning domains. Remarkably, K often allows to model problems more concisely than previous action languages. All the examples given can be tested in an available implementation, the DLV K planning system.

2006

This paper provides a general mechanism and a solid theoretical basis for performing planning within Belief-Desire-Intention (BDI) agents. BDI agent systems have emerged as one of the most widely used approaches to implementing intelligent behaviour in complex dynamic domains, in addition to which they have a strong theoretical background. However, these systems either do not include any built-in capacity for "lookahead" type of planning or they do it only at the implementation level without any precise defined semantics. In some situations, the ability to plan ahead is clearly desirable or even mandatory for ensuring success. Also, a precise definition of how planning can be integrated into a BDI system is highly desirable. By building on the underlying similarities between BDI systems and Hierarchical Task Network (HTN) planners, we present a formal semantics for a BDI agent programming language which cleanly incorporates HTN-style planning as a built-in feature. We argue that the resulting integrated agent programming language combines the advantages of both BDI agent systems and hierarchical offline planners.

Formal Approaches to Agent-Based …, 2002

In this paper we address the task of organising multi-agent systems in order to collectively solve problems. We base our approach on a logical model of rational agency comprising a few simple, but powerful, concepts. While many other researchers have tackled this problem using formal logic, the important aspect of the work described here is that the logical descriptions of the agents are directly executable using the Concurrent MetateM framework, allowing the execution of agents described in a combination of temporal, belief and ability logics. Here, we are particularly concerned with exploring some of the possible logical constraints that may be imposed upon these agents, and how these constraints affect the ability of the agents to come together to collectively solve problems.

1998

We present a logic programming framework implemented over Prolog which is able to model an agent&#x27;s mental state. An agent is modeled by a set of extended logic programming rules representing the agent&#x27;s behavior, attitudes (beliefs, intentions, and goals), world knowledge, and temporal and reasoning procedures. At each stage the agents&#x27;s mental state is defined by the well founded model of the extended logic program plus some constraints. Via this modeling an agent is able to interact with other agents, ...