In this paper we introduce dialectical frameworks, a powerful generalization of Dung-style argumentation frameworks where each node comes with an associated acceptance condition. This allows us to model different types of dependencies, e.g. support and attack, as well as different types of nodes within a single framework. We show that Dung's standard semantics can be generalized to dialectical frameworks, in case of stable and preferred semantics to a slightly restricted class which we call bipolar frameworks. We show how acceptance conditions can be conveniently represented using weights respectively priorities on the links and demonstrate how some of the legal proof standards can be modeled based on this idea.

Dynamic epistemic logic deals with the representation of situations in a multi-agent and dynamic setting. It allows to express in a uniform way statements about: 1. what is true about an initial situation 2. what is true about an event occurring in this situation 3. what is true about the resulting situation after the event has occurred. We axiomatize in this framework what we can infer about (3) given (1) and (2), introducing thereby new techniques to prove completeness. We also show that this axiomatization is decidable. Besides being useful for reasoning about actions, it provides a natural characterization of the product update of dynamic epistemic logic.

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.

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.

Forward State Planning with Reachability Heuristics is arguably the most successful approach to Automated Planning up to date. In addition to an estimation of the distance to the goal, relaxed plans obtained with such heuristics provide the search with useful information such as helpful actions and look-ahead states. However, this information is extracted only from the beginning of the relaxed plan. In this paper, we propose using information extracted from the last actions in the relaxed plan to generate intermediate goals backwards. This allows us to use information from previous computations of the heuristic and reduce the depth of the search tree.

Goal utility dependencies arise when the utility of achieving a goal depends on the other goals that are achieved with it. This complicates the planning procedure because achieving a new goal can potentially alter the utilities of all the other goals currently achieved. In this paper, we present an encoding procedure that enables general-purpose Max-SAT solvers to be used to solve planning problems with goal utility dependencies. We compare this approach to one using integer programming via an empirical evaluation using benchmark problems from past international planning competitions. Our results indicate that this approach is competitive and sometimes more successful than an integer programming one -- solving two to three times more subproblems in some domains, while being outperformed by only a significantly smaller margin in others.

We present a planning domain that encodes the problem of generating natural language sentences. This domain has a number of features that provoke fairly unusual behavior in planners. In particular, hitherto no existing automated planner was sufficiently effective to be of practical value in this application. We analyze in detail the reasons for ineffectiveness in FF, resulting in a few minor implementation fixes in FF's preprocessor, and in a basic reconfiguration of its search options. The performance of the modified FF is up to several orders of magnitude better than that of the original FF, and for the first time makes automated planners a practical possibility for this application. Beside thus highlighting the importance of preprocessing and automated configuration techniques, we show that the domain still poses several interesting challenges to the development of search heuristics.

We introduce the Scanalyzer planning domain, a domain for classical planning which models the problem of automatic greenhouse logistic management. At its mathematical core, the Scanalyzer domain is a permutation problem with striking similarities to common search benchmarks such as Rubik's Cube or TopSpin. At the same time, it is also a real application domain, and efficient algorithms for the problem are of considerable practical interest. The Scanalyzer domain was used as a benchmark for sequential planners at the last International Planning Competition. The competition results show that domain-independent automated planners can find solutions of comparable quality to those generated by specialized algorithms developed by domain experts, while being considerably more flexible.

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.

We empirically examine several ways of exploiting the information of multiple heuristics in a satisficing best-first search algorithm, comparing their performance in terms of coverage, plan quality, speed, and search guidance. Our results indicate that using multiple heuristics for satisficing search is indeed useful. Among the combination methods we consider, the best results are obtained by the alternation method of the "Fast Diagonally Downward" planner.