Since the reinvigoration of emotions research, many computationalmodels of emotion have been developed. None ofthese models, however, fully address the integration of emotiongeneration and emotional effect in the context of cognitiveprocesses. This paper seeks to unify various modelsof computational emotions while fully integrating with workdone in cognitive architectures. We propose a perspective onhow this integration would occur and EmoCog, a cognitivearchitecture with mechanisms for emotion generation and effects.

Behavior-based and reactive control methods are popular choices for building fast and lightweight intelligent controllers for resource-constrained systems. Reactive methods are extremely useful in highly resource-constrained applications, but at a cost: they tend to be even more susceptible to certain types of failures than deliberative techniques. Without a planner to adapt to changes, even a small failure can result in incorrect behavior from the entire controller. In this paper, we propose extensions to behavior-based subsumption that can detect four types of failures.

Emotions are an integral part of human decision making. It follows that emotions should take part in the decision process towards the design of more realistic artificial agents. Three psychological models for emotions are examined and a corresponding algorithm is developed for each depicting its process. A generalized multi-agent model is designed to demonstrate the implementation of each of the three methods. An agent thus represents a human capable of exhibiting emotional state in response to an arbitrary emotionally charged event of varying impact.

We address the problem of simulate democratic elections via a set of competing agents.We propose a logical model based on a set of non-cooperative agents which compete for attracting a maximum number of votes from a population. Each agent builds a set of strategies (formed by the promises, actions and proposals of the agent) used to convince to the potential voters.

Multi-robot task allocation is an important problem for heterogeneous mobile robots. Simultaneous allocations with which multiple tasks are being allocated concurrently tend to lead to more efficient allocations than online or single task allocations. However, the simultaneous allocation also increases the complexity in the winner determination process, especially when robots are required to collaborate in order to accomplish certain tasks. This paper presents a winner determination algorithm for the simultaneous allocation of multi-robot tasks. The complete approach layers alow-level coalition formation algorithm for solving one multi-robot task with a high-level simultaneous task allocation approach. We implement a tree-based winner determination algorithm with an iterative deepening A* (IDA*) search and show that the algorithm is able to generate the optimal task-coalition mapping in the initial round and the IDA* performs efficiently based on time and space complexities.

We revisit a framework for designing peer-based intelligent tutoring systems motivated by McCalla's ecological approach, where learning is facilitated by the previous experiences of peers with a corpus of learning objects. Prior research demonstrated the value of a proposed algorithm for modeling student learning and for selecting the most beneficial learning objects to present to new students. In this paper, we first adjust the validation of this approach to demonstrate its ability to cope with errors in assessing the learning of student peers. We then deepen the representation of learning objects to reflect the expected time to completion and demonstrate how this may lead to more effective selection of learning objects for students, and thus more effective learning. As part of our exploration of these new adjustments, we offer insights into how the size of learning object repositories may affect student learning, suggesting future extensions for the model and its validation.

Although freewriting strategies are commonly taught in composition courses, there have been few empirical studies on freewriting. We address this gap by examining effects of prior writing skills (as measured by a pre-write essay), freewriting training, text-box size (1, 10, 20 lines), and repetitive writing on freewriting quality. Participants watched an agent-based vicarious learning freewriting instruction video or a control video including brief instructions on freewriting. After training, participants wrote six freewrites, two in each box size. Lesson delivery and text box size did not affect expert human ratings of the freewrites. Furthermore, participants did not benefit from writing successive freewrites regardless of their initial skill level. We describe how these results have been used to inform the design of Writing-Pal, an essay-writing intelligent tutoring system.

We investigate a hybrid between temporal partial-order and forward-chaining planning where each action in a partially ordered plan is associated with a partially defined state. The focus is on centralized planning for multi-agent domains and on loose commitment to the precedence between actions belonging to distinct agents, leading to execution schedules that are flexible where it matters the most. Each agent, on the other hand, has a sequential thread of execution reminiscent of forward-chaining. This results in strong and informative agent-specific partial states that can be used for partial evaluation of preconditions as well as precondition control formulas used as guidance. Empirical evaluation shows the resulting planner to be competitive with TLplan and TALplanner, two other planners based on control formulas, while using a considerably more expressive and flexible plan structure.

Usually a voting rule requires agents to give their preferences as linear orders. However, in some cases it is impractical for an agent to give a linear order over all the alternatives. It has been suggested to let agents submit partial orders instead. Then, given a voting rule, a profile of partial orders, and an alternative (candidate) c, two important questions arise: first, is it still possible for c to win, and second, is c guaranteed to win? These are the possible winner and necessary winner problems, respectively. Each of these two problems is further divided into two sub-problems: determining whether c is a unique winner (that is, c is the only winner), or determining whether c is a co-winner (that is, c is in the set of winners). We consider the setting where the number of alternatives is unbounded and the votes are unweighted. We completely characterize the complexity of possible/necessary winner problems for the following common voting rules: a class of positional scoring rules (including Borda), Copeland, maximin, Bucklin, ranked pairs, voting trees, and plurality with runoff.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents ; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.