A commonsense language for reasoning about causation and rational action

AbstractCommonsense causal discourse requires a language with which to express varying degrees of causal connectedness. This paper presents a commonsense language for reasoning about action and causation whose semantics is expressed by way of counterfactuals. Causal relations are analyzed along several dimensions including notions of resource consumption, degree of responsibility, instrumentality, and degree of causal contribution. Grounding the semantics in a level of counterfactual reasoning is shown to play an important role in constraining the set of allowable event descriptions instantiating reports expressed by any of the relations in the language. These ideas are also applied to a causal analysis of rational action: by adopting an explanatory stance, one can characterize action through descriptions that refer to causal connections between mental states and actions. Such a causal analysis resolves some well-known difficulties in correctly ascribing agency and intentionality. Finally, an implementation is described—used to motivate and refine the theory—in which queries involving causal relations between the activities of agents engaged in purposeful behavior within a microworld can be posed

Kiel Declarative Programming Days 2013

This report contains the papers presented at the Kiel Declarative Programming Days 2013, held in Kiel (Germany) during September 11-13, 2013. The Kiel Declarative Programming Days 2013 unified the following events: * 20th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2013) * 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013) * 27th Workshop on Logic Programming (WLP 2013) All these events are centered around declarative programming, an advanced paradigm for the modeling and solving of complex problems. These specification and implementation methods attracted increasing attention over the last decades, e.g., in the domains of databases and natural language processing, for modeling and processing combinatorial problems, and for high-level programming of complex, in particular, knowledge-based systems

Agent interaction: abstract approaches to modelling, programming and verifying multi-agent systems

Computer systems and their applications are becoming increasingly more complicated. Modern systems often consist of multiple independent parts (hardware and software), which interact with their environment. Computers communicate with other computers, exchange information with and receive commands from their human users and receive information about their physicalor virtual environment. This high degree of interactivity leadsto an inherently larger degree of complexity, which needs to be managed and controlled. An important means to reduce complexity is abstraction. Abstraction meansfinding intuitive concepts to model the complex reality and leaving outunderlying details. In the field of multi-agent systems, in which the work ofthis thesis fits, anthropomorphic abstractions are oftenused. Anagent is an autonomous piece of software, designed and/or built in terms ofanthropomorphic concepts, which interacts with other agents and its environmentin such a way that it takes into account the dynamic circumstances and strives to achieve its aims. In this thesis, we focus on agent interaction. Starting from different viewpoints in the field ofmulti-agent systems, we introduce a number of new abstract concepts for agentinteraction. A danger of using abstraction is that abstract concepts areintroduced without grounding them in the computational reality. Therefore, wetake care to always relate our abstract notions to lower-level concepts. We start in Chapter 2 by anchoring three already existing and popular agentconcepts, which are belief, desire and intention, in externally observableagent behaviour. We provide criteria which formally describe when behaviour of an agent indicates that the agent has a certainmental state (a belief, desire or intention). These criteria can be used by agents themselves to attribute belief, desire and intention to other agents, onthe basis of observed behaviour. Chapter 3 deals with agent verification. As the complexity of agent systems ishigh, verification of these systems is very difficult. We develop two principleswhich aid in making verification of agent systems more manageable. The firstprinciple is language abstraction. We use two logical languages to phraseproperties, an abstract one and a detailed one. Properties in theabstract language are shorter and more intuitive than properties in thedetailed language. The second principle is constructing abstract, generic,reusable systems of properties and proofs. In Chapter 4 we present a new model of agents, which focuses on agentinteraction. Our model explicitly includes the dynamic environment. We have areal-time model: actions have a duration. This means that actions of one or more agents can takeplace during overlapping time frames, leading to harmful interference orbeneficial synergy. Agents can perform group actions, which means that themembers of the group perform individual actions in a coordinated manner. In Chapter 5, we develop the programming language GrAPL (Group AgentProgramming Language), intended to program multi-agent systems in which agentscan form temporary alliances to perform group actions. Before a group actions isperformed, the agents communicate with each other to pose demands on details ofthe action and the composition of the group of actors. The programming languagehas a formal operational semantics. We generalise the idea of Chapter 5 in Chapter 6, by looking at group plansinstead of group actions. A group plan is a composed action, consisting of bothindividual actions and group actions, which are partially ordered in time. Weprovide a new high-level coordination language which heterogeneous agents canuse to discuss group plans and to execute them in a synchronised manner

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence