Cooperation and Competition in ALIAS: A Logic Framework for Agents that Negotiate

This paper presents ALIAS, an agent architecture based on intelligent logic agents, where the main form of agent reasoning is abduction. The system is particularly suited for solving problems where knowledge is incomplete, where agents may need to make reasonable hypotheses about the problem domain and other agents, and where the raised hypotheses have to be consistent for the overall set of agents. ALIAS agents are pro-active, exhibiting a goal-directed behavior, and autonomous, since each one can solve problems using its own private knowledge base. ALIAS agents are also social, because they are able to interact with other agents, in order to cooperatively solve problems. The coordination mechanisms are modeled by means of LAILA, a logic-based language which allows to express intra-agent reasoning and inter-agent coordination. As an application, we show how LAILA can be used to implement inter-agent dialogues, e.g., for negotiation. In particular, LAILA is well-suited to coordinate the process of negotiation aimed at exchanging resources between agents, thus allowing them to execute the plans to achieve their goals.     

Generation and Selection of Abductive Explanations for Non-Omniscient Agents

Among the non-monotonic reasoning processes, abduction is one of the most important. Usually described as the process of looking for explanations, it has been recognized as one of the most commonly used in our daily activities. Still, the traditional definitions of an abductive problem and an abductive solution mention only theories and formulas, leaving agency out of the picture. Our work proposes a study of abductive reasoning from an epistemic and dynamic perspective. In the first part we explore syntactic definitions of both an abductive problem in terms of an agent’s information and an abductive solution in terms of the actions that modify the agent’s information. We look at diverse kinds of agents, including not only omniscient ones but also those whose information is not closed under logical consequence and those whose reasoning abilities are not complete. In the second part, we look at an existing logical framework whose semantic model allows us to interpret the previously stated formulas, and we define two actions that represent forms of abductive reasoning.     

Merging deductive and inductive reasoning for processing textual descriptions of inter-human conflicts

We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive, and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This study demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. Suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.     

Programming Rational Agents in a Modal Action Logic

In this paper we describe a language for reasoning about actions that can be used for modelling and for programming rational agents. We propose a modal approach for reasoning about dynamic domains in a logic programming setting. Agent behavior is specified by means of complex actions which are defined using modal inclusion axioms. The language is able to handle knowledge producing actions as well as actions which remove information. The problem of reasoning about complex actions with incomplete knowledge is tackled and the temporal projection and planning problems is addressed; more specifically, a goal directed proof procedure is defined, which allows agents to reason about complex actions and to generate conditional plans. We give a non-monotonic solution for the frame problem by making use of persistency assumptions in the context of an abductive characterization. The language has been used for implementing an adaptive web-based system.     

A sequential model of bargaining in logic programming

This paper proposes a sequential model of bargaining specifying reasoning processes of an agent behind bargaining procedures. We encode agents’ background knowledge, demands, and bargaining constraints in logic programs and represent bargaining outcomes in answer sets. We assume that in each bargaining situation, each agent has a set of goals to achieve, which are normally unachievable without an agreement among all the agents who are involved in the bargaining. Through an alternating-offers procedure, an agreement among bargaining agents may be reached by abductive reasoning. We show that the procedure converges to a Nash equilibrium if each agent makes rational offers/counteroffers in each round. In addition, the sequential model also has a number of desirable properties, such as mutual commitments, individual rationality, satisfactoriness, and honesty.     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

A logic-based model of intention formation and action for multi-agent subcontracting

We present a formalism for representing the formation of intentions by agents engaged in cooperative activity. We use a syntactic approach presenting a formal logical calculus that can be regarded as a meta-logic that describes the reasoning and activities of the agents. Our central focus is on the evolving intentions of agents over time, and the conditions under which an agent can adopt and maintain an intention. In particular, the reasoning time and the time taken to subcontract are modeled explicitly in the logic. We axiomatize the concept of agent interactions in the meta-language, show that the meta-theory is consistent and describe the unique intended model of the meta-theory. In this context we deal both with subcontracting between agents and the presence of multiple recipes, that is, multiple ways of accomplishing tasks. We show that under various initial conditions and known facts about agent beliefs and abilities, the meta-theory representation yields good results.     

Agent Programming in 3APL

An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.To support this new style of programming, we propose the agent programming language 3APL. 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative and logic programming. From imperative programming the language inherits the full range of regular programming constructs, including recursive procedures, and a notion of state-based computation. States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. These features are well-understood and provide a solid basis for a structured agent programming language. Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.Applying the metaphor of intelligent agents means taking a design stance. From this perspective, a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. We illustrate how the metaphor of intelligent agents is supported by the programming language. We also discuss the design of control structures for rule-based agent languages. A control structure provides a solution to the problem of which goals and which rules an agent should select. We provide a concrete and intuitive ordering on the practical reasoning rules on which such a selection mechanism can be based. The ordering is based on the metaphor of intelligent agents. Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.     

OWL-QL—a language for deductive query answering on the Semantic Web

This paper discusses the issues involved in designing a query language for the Semantic Web and presents the OWL query language (OWL-QL) as a candidate standard language and protocol for query–answering dialogues among Semantic Web computational agents using knowledge represented in the W3Cs ontology web language (OWL). OWL-QL is a formal language and precisely specifies the semantic relationships among a query, a query answer, and the knowledge base(s) used to produce the answer. Unlike standard database and Web query languages, OWL-QL supports query–answering dialogues in which the answering agent may use automated reasoning methods to derive answers to queries, as well as dialogues in which the knowledge to be used in answering a query may be in multiple knowledge bases on the Semantic Web, and/or where those knowledge bases are not specified by the querying agent. In this setting, the set of answers to a query may be of unpredictable size and may require an unpredictable amount of time to compute.     

Towards a generic distributed and collaborative digital manufacturing

A framework for distributed manufacturing is proposed to facilitate collaborative product development and production among geographically distributed functional agents using digitalized information. Considering the complexity of products created in a distributed manufacturing scenario, it often requires close collaborations among a number of facilities. In this research work, various functional agents, such as the manufacturability evaluation agent (MEA), manufacturing resource agent (MRA), process-planning agent (PPA), manufacturing scheduling agent (MSA), shop floor agent (SFA), fault diagnosis agent (FDA), etc., can interact coherently for distributed manufacturing. With specific agents having unique functionalities, a manufacturing managing agent (MMA) acts as the centre of this distributed manufacturing system. The MMA agent assists the specific agents’ to work seamlessly and also to collaborate closely with the participating agents. In this way, the production cycle of a part can be optimized from product design to final manufacturing since all the production procedures are considered logically and every procedure is correlated. The agent language based on the knowledge query manipulation language (KQML) includes many pre-defined performatives that ease the participating agents to carry out their tasks intelligently by interpreting commands from one another. Additionally, to ensure the adaptiveness and upgradeability of the system, the internal structure of each functional agent that is based on JATLite is modularized into several components, including a communication interface, central work engine, knowledge base pool, and input/output modifier for possible future methodology enhancements.     

Using compiled knowledge to guide and focus abductive diagnosis

Several artificial intelligence architectures and systems based on “deep” models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational complexity. One of the ways to face this problem is to rely on a knowledge compilation phase, which produces knowledge that can be used more effectively with respect to the original one. We show how a specific knowledge compilation approach can focus reasoning in abductive diagnosis, and, in particular, can improve the performances of AID, an abductive diagnosis system. The approach aims at focusing the overall diagnostic cycle in two interdependent ways: avoiding the generation of candidate solutions to be discarded a posteriori and integrating the generation of candidate solutions with discrimination among different candidates. Knowledge compilation is used off-line to produce operational (i.e., easily evaluated) conditions that embed the abductive reasoning strategy and are used in addition to the original model, with the goal of ruling out parts of the search space or focusing on parts of it. The conditions are useful to solve most cases using less time for computing the same solutions, yet preserving all the power of the model-based system for dealing with multiple faults and explaining the solutions. Experimental results showing the advantages of the approach are presented     

Design and Implementation of a Hybrid Agent Platform

This paper presents IMAP, a hybrid agent platform composed of several cooperating intelligent agents and mobile agents. IMAP is implemented in Java and Prolog. Java is used to implement the framework of the system, and in particular for supporting the communication between agents and mobility of agent, while Prolog is used to implement both adduction and derivation mechanisms. IMAP intends to independently employ the underlying derivation/adduction and mobility mechanism. In IMAP, intelligent agent and mobile agent can not only fully exploit individual virtue, but also cooperate to perform a task under a uniform platform. Intelligent agents in IMAP are equipped with hypothetical reasoning capabilities, performed by means of adduction: if the knowledge available to an agent is insufficient to solve a query, the agent could adduce new hypotheses. Each intelligent agent can accept queries from mobile agents by means of the interface module, each query is passed to the reasoning module of intelligent agent which performs a derivation and adduction in order to get an answer for mobile agent. IMAP also provides mobile agents a flexible and efficient coordination mechanism and a reliable migration mechanism, and supports persistence of agent state and agent security. Mobile agent's coordination mechanism exploits the advantages of the XML language and Linda-like coordination. This programmable Linda-like coordination mechanism suits the mobility and openness of the Internet application, XML standard for Internet data representation may guarantee a high-degree of interoperability between heterogeneous environments. The design and implementation key technologies of IMAP are described in this paper. An Internet based auction application example shows the suitability and the effectiveness of the IMAP, and its performance evaluation is also made. Finally, some conclusions and remarks are given.     

Distributed reasoning with ontologies and rules in order-sorted logic programming

Integrating ontologies and rules on the Semantic Web enables software agents to interoperate between them; however, this leads to two problems. First, reasoning services in SWRL (a combination of OWL and RuleML) are not decidable. Second, no studies have focused on distributed reasoning services for integrating ontologies and rules in multiple knowledge bases. In order to address these problems, we consider distributed reasoning services for ontologies and rules with decidable and effective computation. In this paper, we describe multiple order-sorted logic programming that transfers rigid properties from knowledge bases. Our order-sorted logic contains types (rigid sorts), non-rigid sorts, and unary predicates that distinctly express essential sorts, non-essential sorts, and non-sortal properties. We formalize the order-sorted Horn-clause calculus for such properties in a single knowledge base. This calculus is extended by embedding rigid-property derivation for multiple knowledge bases, each of which can transfer rigid-property information from other knowledge bases. In order to enable the reasoning to be effective and decidable, we design a query-answering system that combines order-sorted linear resolution and rigid-property resolution as top-down algorithms.     

基于双向推理的主体框架

文章提出了一个基于双向推理的主体框架FBRA,它是一个混合型的主体框架,主体既是反应的又是慎思的。它的推理内核是正向推理和反向推理相结合。正向推理用于对环境的反应,包括对其他主体的反应。反向推理基于溯因推理,用于信念修正、规划、多主体协调和多主体通信等。     

Multi-modal nonmonotonic logics of minimal knowledge

In this paper we introduce multi-modal logics of minimal knowledge. Such a family of logics constitutes the first proposal in the field of epistemic nonmonotonic logic in which the three following aspects are simultaneously addressed: (1) the possibility of formalizing multiple agents through multiple modal operators; (2) the possibility of using first-order quantification in the modal language; (3) the possibility of formalizing nonmonotonic reasoning abilities for the agents modeled, based on the principle of minimal knowledge. We illustrate the expressive capabilities of multi-modal logics of minimal knowledge to provide a formal semantics to peer-to-peer data integration systems, which constitute one of the most recent and complex architectures for distributed information systems.      

基于溯因推理的主体协商

为了能够进行有效的协商，主体应当提高通信的效率。为此，接收者可以对发送者的当时的意识状态进行推测，这可以通过溯因推理实现。该文提出了一个基于溯因推理的主体协商模型，是对Parsons的基于论据的协商模型的改进。     

Being and acting rational [agent design]

Rationality alone is insufficient to specify agent design. Using economic theory, we can program agents to behave in ways that maximize their utility while responding to environmental changes. However, economic models for agents, although general in principle, are typically limited in practice because the value functions that are tractable essentially reduce an agent to acting selfishly. Building a stable social system from a collection of agents motivated by self-serving interests is difficult. Finally, understanding rationality and knowledge requires interdisciplinary results from artificial intelligence, distributed computing, economics and game theory, linguistics, philosophy, and psychology. A complete theory involves semantic models for knowledge, belief, action, uncertainty; bounded rationality and resource-bounded reasoning; commonsense epistemic reasoning; reasoning about mental states; belief revision; and interactions in multiagent systems.     

Multiple Adaptive Agents for Tactical Driving

Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However, there is currently no system for tactical-level reasoning. Such a system should address tasks such as passing cars, making exits on time, and merging into a traffic stream. Many previous approaches have attempted to hand construct large rule-based systems which capture the interactions between multiple input sensors, dynamic and potentially conflicting subgoals, and changing roadway conditions. However, these systems are extremely difficult to design due to the large number of rules, the manual tuning of parameters within the rules, and the complex interactions between the rules. Our approach to this intermediate-level planning is a system which consists of a collection of autonomous agents, each of which specializes in a particular aspect of tactical driving. Each agent examines a subset of the intelligent vehicle's sensors and independently recommends driving decisions based on their local assessment of the tactical situation. This distributed framework allows different reasoning agents to be implemented using different algorithms.When using a collection of agents to solve a single task, it is vital to carefully consider the interactions between the agents. Since each reasoning object contains several internal parameters, manually finding values for these parameters while accounting for the agents' possible interactions is a tedious and error-prone task. In our system, these parameters, and the system's overall dependence on each agent, is automatically tuned using a novel evolutionary optimization strategy, termed Population-Based Incremental Learning (PBIL).Our system, which employs multiple automatically trained agents, can competently drive a vehicle, both in terms of the user-defined evaluation metric, and as measured by their behavior on several driving situations culled from real-life experience. In this article, we describe a method for multiple agent integration which is applied to the automated highway system domain. However, it also generalizes to many complex robotics tasks where multiple interacting modules must simultaneously be configured without individual module feedback.