We often deal with dynamic worlds where actions are executed by agents and events may happen. Example of such worlds range from virtual worlds such as the world of a database to robots and humans in physical worlds. To understand the dynamics of such worlds as well as to be able to assert some control over such worlds one needs to reason about the actions and events and how they may change the world. In this invited talk we will present some of the important results in this field and present some future directions. In particular, we will discuss how theories and results from reasoning about actions and change can be combined with theories and results in dynamic epistemic logics to obtain a unified theory of multi-agent actions.

Invited Talk by Ian Horrocks Ontologies and ontology based systems are rapidly becoming mainstream technologies, with RDF and OWL now being deployed in diverse application domains, and with major technology vendors starting to augment their existing systems with ontological reasoning. For example, Oracle Inc. recently enhanced its well-known database management system with modules that use RDF/OWL ontologies to support "semantic data management," and their product brochure lists numerous application areas that can benefit from this technology, including enterprise information integration, knowledge mining, finance, compliance management and life science research. While gratifying to the KR research community, this success also brings with it many challenges. In particular, ontology reasoning systems will need to exhibit robust scalability if deployments in large scale applications are to be successful.

A lot of mathematical knowledge has been formalized and stored in repositories by now: different mathematical theorems and theories have been taken into consideration and included in mathematical repositories. Applications more distant from pure mathematics, however --- though based on these theories --- often need more detailed knowledge about the underlying theories. In this paper we present an example Mizar formalization from the area of electrical engineering focusing on stability theory which is based on complex analysis. We discuss what kind of special knowledge is necessary here and which amount of this knowledge is included in existing repositories.

In many networks, vertices have hidden attributes, or types, that are correlated with the networks topology. If the topology is known but these attributes are not, and if learning the attributes is costly, we need a method for choosing which vertex to query in order to learn as much as possible about the attributes of the other vertices. We assume the network is generated by a stochastic block model, but we make no assumptions about its assortativity or disassortativity. We choose which vertex to query using two methods: 1) maximizing the mutual information between its attributes and those of the others (a well-known approach in active learning) and 2) maximizing the average agreement between two independent samples of the conditional Gibbs distribution. Experimental results show that both these methods do much better than simple heuristics. They also consistently identify certain vertices as important by querying them early on.

Luhmann (1984) defined society as a communication system which is structurally coupled to, but not an aggregate of, human action systems. The communication system is then considered as self-organizing ("autopoietic"), as are human actors. Communication systems can be studied by using Shannon's (1948) mathematical theory of communication. The update of a network by action at one of the local nodes is then a well-known problem in artificial intelligence (Pearl 1988). By combining these various theories, a general algorithm for probabilistic structure/action contingency can be derived. The consequences of this contingency for each system, its consequences for their further histories, and the stabilization on each side by counterbalancing mechanisms are discussed, in both mathematical and theoretical terms. An empirical example is elaborated.

We present an extension of the planning framework based on action graphs and local search to deal with PDDL2.1 temporal problems requiring concurrency, while previously the approach could only solve problems admitting a sequential solution. The paper introduces a revised plan representation supporting concurrency and some new search techniques using it, which are implemented in a new version of the LPG planner. An experimental analysis indicates that the proposed approach is suitable to temporal planning with requiring concurrency and is competitive with state-of-the-art planners.

Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC-2008 used the cost of the best known plan divided by the cost of the generated plan as an evaluation metric. This paper proposes and evaluates two simple but effective methods for plan improvement: Action Elimination improves an existing plan by repeatedly removing sets of irrelevant actions. Plan Neighborhood Graph Search finds a new, shorter plan by creating a plan neighborhood graph PNG(π) of a given plan π, and then extracts a shortest path from PNG(π). Both methods are implemented in the Aras postprocessor and are empirically shown to improve the result of several planners, including the top four planners from IPC-2008, under competition conditions.

Disaster Response Robotics is a challenging domain, where distributed. New challenges hence come up in terms the need for intelligent robotic agents (as opposed to just of autonomy, cooperation and collective behaviors. In the robots) is motivated both by technical considerations and in first part of the talk, I briefly overview the state of the art a practical application perspective. In emergency scenarios in the field of disaster response robotics, in order to support time is critical. Hence, there is a great demand for tools the above sketched analysis. In the second part of that improve the effectiveness of operations. Although there the talk, I present some of the research we developed at are specific actions that can be accomplished by a robot, Sapienza Univ. of Rome, also in collaboration with the Italian such as for example bomb disposal, a key goal of disaster Firemen Department. Specifically, I describe some results response robots is to acquire knowledge about the scenario.

While the string of successes found in using heuristic, best-first search methods have provided positive reinforcement for continuing work along these lines, fundamental problems arise when handling objectives whose value does not change with search operations. An extreme case of this occurs when handling the objective of generating a temporal plan with short makespan. Typically used heuristic search methods assume strictly positive edge costs for their guarantees on completeness and optimality, while the usual ``fattening'' and ``advance time'' steps of heuristic search for temporal planning have the potential of resulting in ``g-value plateaus''. In this paper we point out some underlying difficulties with using modern heuristic search methods when operating over g-value plateaus and discuss how the presence of these problems contributes to the poor performance of heuristic search planners. To further illustrate this, we show empirical results on recent benchmarks using a planner made with makespan optimization in mind.

Evolutionary trees of species can be reconstructed by pairwise comparison of their entire genomes. Such a comparison can be quantified by determining the number of events that change the order of genes in a genome. Earlier Erdem and Tillier formulated the pairwise comparison of entire genomes as the problem of planning rearrangement events that transform one genome to the other. We reformulate this problem as a planning problem to extend its applicability to genomes with multiple copies of genes and with unequal gene content, and illustrate its applicability and effectiveness on three real datasets: mitochondrial genomes of Metazoa, chloroplast genomes of Campanulaceae, chloroplast genomes of various land plants and green algae.